In an era defined by data-driven decision-making, the ability to extract meaningful knowledge from complex datasets is more important than ever. Advances in Data Mining and Intelligent Analytics - Methods, Interpretability, and Cross-Domain Applications offers a timely and focused exploration of how modern data mining supports discovery, prediction, and intelligent decision-making across diverse domains. This volume brings together key ideas spanning data visualization, statistical thinking, machine learning, deep learning, and scalable data management, presenting a coherent view of both foundational principles and emerging directions. Readers are introduced to concepts such as exploratory data analysis, predictive modeling, model optimization, adversarial learning, interpretable AI, and data infrastructure for large-scale applications. Emphasizing real-world relevance, the book highlights how data mining can be applied across healthcare, agriculture, industry, and intelligent digital systems. Designed for graduate students, researchers, and professionals, the text balances conceptual understanding with a practical perspective, making it suitable as both a reference and a learning resource. Key advantages of this book include its integration of foundational knowledge with advanced methods, its cross-domain applicability, and its focus on scalable and responsible data use. By connecting core data mining techniques with current AI-driven approaches, this volume helps readers understand not only how methods work but also why they matter in today’s data-rich world. Whether the goal is to strengthen analytical skills, explore modern machine learning strategies, or understand the evolving landscape of intelligent data analysis, this book provides a clear and engaging guide to the field.