Machine learning-based CSI localization techniques is important for enhancing the accuracy, adaptability, and scalability of indoor positioning systems. Building upon the foundational discussions in Chap. 1 , which covered the three areas of this book. This chapter introduces the machine learning algorithms that drive these advancements forward. We explore both supervised and unsupervised learning methods, focusing on representative algorithms such as KNN, SVM, Decision Trees, k-Means, CNN, and RNN. Each technique is examined in terms of its theoretical foundation, practical strengths, and applicability to CSI-based localization. Optimization strategies, including feature selection and parameter tuning, are also discussed to improve overall system performance. A comparative analysis is presented to evaluate algorithmic effectiveness under realistic deployment conditions, addressing several challenges raised in the previous chapter. By integrating these machine learning approaches, we also provide both theoretical insights and practical guidance for developing efficient, robust, and adaptive CSI localization systems. It builds on the basic concepts introduced in Chap. 1 and progresses toward advanced algorithmic solutions, setting the stage for subsequent chapters on offline data collection, intelligent updates, and accurate localization. This chapter is crucial for researchers and practitioners aiming to leverage machine learning for dynamic indoor environments.