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Spatio-temporal crime analysis has emerged as a critical tool for understanding and predicting crime patterns in urban environments. Recent advancements in machine learning and deep learning have significantly improved the accuracy of crime prediction models by capturing complex spatial and temporal dependencies. However, existing approaches primarily focus on hotspot detection and predictive performance, often lacking interpretability and limited integration of environmental risk factors and real-world policing requirements. This paper presents a comprehensive review of recent spatio-temporal crime analysis techniques, including deep learning, graph-based models, and hybrid approaches, highlighting their strengths and limitations. Based on this analysis, a conceptual risk-aware spatio-temporal framework is proposed that integrates crime density, environmental risk factors, and temporal dynamics to enhance analytical depth and practical relevance. The proposed approach emphasizes the identification of crime opportunity zones and incorporates activity-based temporal patterns to better reflect real-world crime behavior. Additionally, the framework aims to bridge the gap between predictive analytics and actionable policing strategies, particularly in data-constrained regions such as Chhattisgarh. This study contributes by combining predictive modeling with interpretability and contextual awareness, providing a foundation for more effective and adaptive crime analysis systems.