Intelligent Data-Driven Decision Making Using Artificial Intelligence and Machine Learning Techniques

Abstract

The rapid growth of data across healthcare, business, finance, and industrial sectors has created an urgent need for intelligent systems capable of extracting meaningful insights and supporting accurate decision-making. Traditional data analysis methods often struggle to handle large-scale, complex, and high-dimensional datasets. Artificial Intelligence (AI) and Machine Learning (ML) techniques have emerged as powerful solutions to address these challenges by enabling data-driven decision-making through predictive, prescriptive, and adaptive models. This research paper explores the role of AI and ML in intelligent decision making systems, highlighting key algorithms, architectures, and application domains. The study discusses supervised, unsupervised, and reinforcement learning models used for decision support, evaluates their effectiveness through comparative analysis, and examines real-world use cases in healthcare, business intelligence, and big data analytics. Furthermore, ethical considerations, interpretability challenges, and future research directions are discussed to provide a holistic understanding of AI-driven decision systems. The findings indicate that AI and ML significantly enhance decision accuracy, efficiency, and scalability, making them indispensable tools for modern intelligent systems.