Intelligent Finance: AI and ML Approaches to Decision-Making explores the transformative role of artificial intelligence (AI) and machine learning (ML) in modern financial systems. The book examines how data-driven models, predictive analytics, algorithmic trading strategies, risk assessment frameworks, and intelligent automation are reshaping financial decision-making across banking, investment management, insurance, and fintech ecosystems. It provides a structured understanding of core AI and ML techniques—such as supervised and unsupervised learning, neural networks, natural language processing, and reinforcement learning—and demonstrates their practical applications in credit scoring, fraud detection, portfolio optimization, market forecasting, and behavioral finance analysis. Beyond technical implementation, the book critically addresses model interpretability, data governance, regulatory compliance, cybersecurity risks, and ethical considerations associated with automated financial systems. Designed for students, researchers, finance professionals, and technology practitioners, this work bridges theoretical foundations with real-world applications, offering a comprehensive framework for integrating intelligent systems into strategic and operational financial decision-making in an increasingly complex and competitive global environment.