In today's interconnected digital landscape, where technology plays an increasingly crucial role in our lives, the realms of public safety and law enforcement have undergone a profound transformation. This digital convergence has opened new avenues for advanced analytics, providing valuable insights into various aspects of society, including a holistic understanding of crime dynamics. However, traditional crime analytics methods fall short in keeping up with the complexities of modern criminal activities. These methods are limited by their isolation and reliance on similar types of data and resources, making it difficult to adapt to our fast-moving, interconnected society. In response, our project presents a novel and comprehensive approach that transcends these barriers. By ensuring the protection of data and optimizing the efficiency of crime analytics processes, this project captures valuable information from diverse data sources, thereby facilitating a more robust understanding of crime patterns. The developed technologies from this research can be applied to many other fields such as healthcare, finance, and environmental monitoring. The project will integrate research with education and outreach to different groups including unrepresentative and minority students. The focus of this project is to enhance AI-driven crime analytics through the development of a federated meta-learning framework. This framework will facilitate efficient, private, and secure model trainin