Latest research in Criminal Analysis and Prediction using Machine Learning focuses on leveraging advanced machine learning and deep learning techniques to analyze historical crime data, identify patterns, predict crime hotspots, and assess risks in various contexts. Studies employ models such as CNN-LSTM hybrids, Graph Attention Networks (GATs), and other predictive algorithms to capture spatial-temporal dependencies, complex relationships within criminal networks, and trends in criminal activity. Research also emphasizes the integration of big data analytics, data fusion, and systematic evaluations to improve forecasting accuracy and inform proactive policing strategies. These approaches aim to enhance public safety, optimize law enforcement resource allocation, and provide actionable insights for crime prevention and decision-making.