In the contemporary era, machine learning has emerged as a transformative force, reshaping industries and redefining the paradigms of safety and efficiency. Particularly in urban transportation, where the adoption of micro-mobility solutions like micro-electric vehicles has surged, the integration of machine learning techniques offers promising avenues to address emerging challenges. This thesis presents a pioneering approach to harnessing the power of machine learning for the design and deployment of a real-time monocular collision alert system tailored to enhance safety standards in micro-mobility transportation. Advanced object detection and tracking algorithms, underpinned by machine learning principles, form the core of this system, enabling it to swiftly and accurately identify potential collision threats in dynamic urban settings.