In the cutting edge domain that is the ever so expanding smart city, the need for development tools that can keep up with such an urban environment is ever so prevalent. Moreover, the rapid expansion of such metropolitan areas requires even more specialists than are currently available. This study presents a pioneering cloud-based solution for intelligent transportation and crime prevention, emphasizing the seamless integration of machine learning techniques within a web deploy-ment framework. Utilizing data from sources like Automated Speed Enforcement, police crime statistics, and traffic moni-toring programs, our approach employs advanced predictive analytics to accurately identify potential crime hotspots and optimize traffic management. A significant innovation of this research is the development of a scalable Software as a Service model, which allows for the effective predictions of traffic sensors across urban settings. The proposed system features a user-friendly graphical user interface and employs Dynamic Load Balancing to enhance computational efficiency, making it accessible to a wide range of users. By harnessing cloud computing, our solution offers a versatility for government officials, law enforcement, and researchers, promising improvements in road safety, crime prevention, and the overall quality of life.