This chapter presents a comprehensive overview of techniques used in medical imaging analysis, spanning classical machine learning and deep learning approaches. It covers traditional feature extraction methods, including intensity, texture, shape, frequency-domain features, and anatomical descriptors. Deep learning techniques, such as convolutional neural networks and vision transformers, are discussed for their ability to learn hierarchical image representations. Image augmentation strategies, including spatial, intensity-based, and advanced simulation techniques, are reviewed to enhance model generalizability. The chapter concludes with a discussion on explainable AI, highlighting the importance of transparency and interpretability in medical image analysis and clinical decision support.