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This review paper surveys the integration of machine learning techniques in spatial data mining, a crucial intersection of geographic information systems and data mining. It examines the application of various machine learning algorithms such as classification, regression, clustering, and deep learning in spatial data analysis. The paper discusses challenges like data preprocessing, feature selection, and model interpretability, alongside recent advancements including spatial-temporal analysis and heterogeneous data integration. Through critical analysis of existing literature, it identifies trends, methodologies, and future research directions. Practical implications and applications across domains like urban planning, environmental monitoring, and epidemiology are explored. As a comprehensive resource, this review facilitates understanding and utilization of machine learning approaches for extracting insights from spatial data, benefiting researchers, practitioners, and policymakers alike.