The Applications and Challenges of Machine Learning in Intelligent Decision-Making

Machine learning has become a central technology for transforming data into intelligent decisions in modern organisations. Recent advances in deep learning and reinforcement learning have greatly expanded the capacity of machines to recognise patterns, forecast outcomes and optimise sequential choices, which has stimulated a wide range of decision-support applications. By adopting a structured review method, this paper reviews the applications and challenges of machine learning in intelligent decision-making. The results show that learning-based models improve the accuracy, speed and scalability of decisions in clinical diagnosis, asset management, supply-chain planning and autonomous driving. The analysis also identified four recurring challenges that limit practical adoption, namely data quality, model interpretability, algorithmic fairness and safety. The paper concluded that reliable deployment depends on transparent model design, fairness auditing and human-in-the-loop governance, and it proposed directions for future research in hybrid human-machine decision systems.