Artificial Neural Networks Market Forecast Shows Robust Growth to USD 600.3 Million by 2030 at 17.6% CAGR, Led by Healthcare and Automotive Applications | Key Players - NVIDIA Corporation, IBM Corporation, Google LLC

Global Artificial Neural Networks (ANN) Market reached US$ 164.3 million in 2022 and is expected to reach US$ 600.3 million by 2030 growing with a CAGR of 17.6% during the forecast period 2024-2031.The Artificial Neural Networks (ANN) market is driven by rising AI adoption, increasing big data analytics, growing demand for predictive modeling, advancements in deep learning, expanding cloud computing infrastructure, and wider applications across healthcare, finance, automotive, and cybersecurity sectors.

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Key Industry Developments

✅ December 2025: AWS unveiled Nova 2 AI models (Lite, Pro, Sonic, Omni) at re:Invent, leveraging advanced neural network architectures for multimodal reasoning, speech-to-speech processing, and real-time conversational AI, enhancing enterprise AI workloads.

✅ November 2025: Keysight released ADS 2025 software with Python AI training for Artificial Neural Network (ANN) models in nonlinear circuit simulation, enabling automated workflow customization and machine learning from measured data for RF/microwave designs.

✅ August 2025: Google Cloud launched Gemini Data Agents including Data Engineering and Conversational Analytics Agents, powered by neural networks for data cleaning, analysis, and plain-language querying in BigQuery workflows.

✅ December 2025: Rakuten Group launched Rakuten AI 3.0, Japan's top-scoring large language model optimized for Japanese benchmarks, achieving 90% cost reduction in ecosystem services via neural network advancements under the GENIAC project.

✅ October 2025: Japan's government released its 2025 AI Strategy, boosting domestic LLM development with neural networks tailored for cultural nuance, alongside local AI use cases in health and administration.

✅ August 2025: Market Research Future reported Japan ANN market growth to USD 8.97 billion, driven by integrations in manufacturing, robotics, and Society 5.0 initiatives by firms like Sony and Hitachi.

Key M&A Deals

✅ Advanced Micro Devices (AMD) acquired ZT Systems for US$ 4.9 billion, announced in August 2025, to strengthen its AI infrastructure capabilities, enhance data center server design expertise, and accelerate deployment of ANN-optimized compute platforms for hyperscalers and enterprise AI workloads.

✅ Synopsys completed its acquisition of Ansys valued at approximately US$ 35 billion, finalized in July 2025, integrating AI-driven simulation and semiconductor design tools to support ANN hardware development and advanced chip validation environments.

✅ NVIDIA Corporation completed its acquisition of Run:ai in April 2025 (deal value reported at US$ 700 million), enhancing AI workload orchestration, GPU virtualization, and scalable ANN training optimization across cloud and on-premise environments.

✅ IBM acquired HashiCorp for approximately US$ 6.4 billion, completed in February 2025, strengthening hybrid cloud automation and infrastructure provisioning to support enterprise deployment of ANN-based AI applications.

Key Players:-

Microsoft | IBM | SAS Institute | Oracle | Splunk | Riverbed Technology | NetScout Systems

Key Segments

By component, the market is divided into hardware, software, and services. Hardware includes processors such as GPUs and specialized AI chips that support high-speed neural network training and inference. Software comprises ANN frameworks, platforms, and development tools, while services include integration, consulting, and maintenance offerings that help organizations deploy and optimize neural network solutions.

By deployment mode, the market is categorized into on-premises and cloud-based solutions. On-premises deployment is preferred by organizations requiring higher data security and regulatory compliance, while cloud deployment is gaining traction due to scalability, cost-efficiency, and easier access to advanced computing resources for AI model development and execution.

By application, ANN is widely used in image and speech recognition, natural language processing, predictive analytics, fraud detection, and robotics. These applications enable automation, intelligent decision-making, and real-time data analysis across multiple sectors, driving strong demand for neural network technologies.

By end-user industry, the market serves healthcare, BFSI, retail, IT & telecommunications, manufacturing, automotive, and others. Healthcare leverages ANN for diagnostics and drug discovery, BFSI for risk analysis and fraud detection, retail for customer behavior analysis, and automotive for autonomous driving and advanced driver-assistance systems.

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Market Drivers

• Exponential Growth in Data Availability: Rapid increase in structured and unstructured data from digital platforms, IoT devices, and enterprise systems is fueling demand for ANN-powered analytics and pattern recognition solutions. ANNs help organizations extract meaningful insights and make data-driven decisions.

• Advancements in Computational Power and Infrastructure: Improvements in GPUs, TPUs, cloud computing, and high-performance hardware are enabling faster training and deployment of deep learning models, significantly accelerating ANN adoption across industries.

• Rising Adoption of AI and Automation across Industries: Businesses are increasingly deploying ANNs for intelligent automation, predictive analytics, process optimization, and decision support in sectors such as healthcare, finance, manufacturing, and telecom.

• Increasing Use of Cloud and Edge AI Solutions: Cloud-based ANN solutions offer scalability, cost-efficiency, and remote accessibility, while edge AI integration enables real-time analytics and intelligent processing on devices, expanding ANN applications further.

• Demand for Advanced Pattern Recognition and Predictive Capabilities: The need to detect complex patterns in speech, image, and language data is driving investment in ANN architectures for use cases such as computer vision, natural language processing, autonomous systems, and personalized recommendations.

• Supportive Investments and Regulatory Initiatives: Growing investments from government and private sectors in AI research, infrastructure, and digital transformation strategies are encouraging the development and implementation of neural network solutions globally.

Regional Insights

North America: 35% (Largest share, driven by widespread adoption of AI-driven neural network solutions across BFSI, IT, healthcare, and advanced R&D ecosystems).

Asia Pacific: 30% (Fastest-growing region, fueled by rapid AI investment in China, India, South Korea, and Japan and expanding digital transformation initiatives).

Europe: 25% (Strong contribution supported by industrial automation, AI research hubs, and regulatory support for ethical AI deployment).

Latin America: 5% (Steady growth with increasing AI adoption in finance, logistics, and SME segments).

Middle East & Africa: 5% (Emerging adoption driven by smart city initiatives, government AI strategies, and enterprise automation investments).

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