In response to the evolving landscape of criminal activities that compromise community welfare and safety, this study proposes an advanced methodology that integrates traditional data mining techniques with blockchain technology for the analysis and prediction of theft crime trends. Utilizing a dataset of theft crimes, this research employs classification, clustering, and rule mining association to identify key insights into crime hotspots and factors influencing theft incidents. The pioneering application of blockchain technology enhances the integrity and transparency of digital evidence management within the digital forensics process. This innovative approach not only improves the strategic allocation of law enforcement resources but also establishes a new benchmark for merging data mining and blockchain in crime analysis, providing a robust framework to strengthen public safety and crime prevention strategies.