Latest research in Criminal Pattern Detection using Data Mining focuses on leveraging advanced machine learning, deep learning, and data mining techniques to analyze historical crime data and identify patterns, trends, and correlations that can aid law enforcement in predicting and preventing criminal activities. Studies employ algorithms such as Random Forest, K-Means clustering, and hybrid deep learning models like LCRNet, integrating Transformer and CNN architectures with sparsity optimization, to efficiently recognize complex crime patterns. Research also emphasizes spatial-temporal analysis, predictive modeling, and the careful selection of appropriate algorithms to improve detection accuracy and reduce false positives. These data-driven approaches aim to enhance public safety, optimize resource allocation for law enforcement, and provide actionable insights for proactive crime prevention strategies.