Hyperspectral imaging (HSI) provides detailed spectral information that enables accurate material identification and scene analysis. However, the high dimensionality, spectral variability, noise, and limited labeled data associated with hyperspectral datasets pose major challenges for traditional analysis techniques. Machine learning has emerged as an effective solution to address these challenges and improve classification and interpretation performance. Machine Learning Techniques for Hyperspectral Image Analysis: Algorithms, Feature Engineering, and Practical Implementations presents a structured introduction to classical machine learning methods for hyperspectral data analysis. The book covers the complete workflow, including hyperspectral data representation, preprocessing, feature extraction, dimensionality reduction, classification, and performance evaluation. The text emphasizes widely used techniques such as PCA, ICA, MNF, Support Vector Machines, Random Forests, and ensemble learning methods, with practical insights into model selection, hyperparameter optimization, and validation strategies. Benchmark datasets and case studies are included to demonstrate real-world implementations and comparative analysis. This book is intended for students, researchers, and professionals in remote sensing, image processing, and machine learning, serving as both a learning resource and a practical reference for hyperspectral image analysis.