Two-wheeler riders continue to be among the most vulnerable groups of road users internationally, and poor visibility and limited protection in the event of a crash result in high accident rates. Enhanced safety demands not only technology to improve rider vision but also technology that predicts and averts accidents through proactive sensing and communication. In this work, we aim to conduct an exhaustive study of automatic headlamp control and anti-collision systems designed for two-wheeled vehicles. The work is structured as follows: A general introduction from the point of view of illumination and rider perception is presented, and then in a second section, which describes in detail autonomous headlamp management strategic solutions developed in recent years, such as light-dependent resistors, camera-based solutions, mmWave radar- vision fusion, and wearable sensor systems. Then, anti-collision technologies are considered, including ultrasonic sensors, radar, vision-based object identification, and IoT-based vehicle-to-infrastructure communication. The review highlights the benefits, shortcomings, and possible trade-offs of these tools in terms of cost, accuracy, scalability, and practicality. Finally, the influence of IoT connectivity and edge-intelligent enhancements is investigated, demonstrating how Real-time (RT) processing and communication can enhance safety performance. This work identifies unresolved research issues related to sensor robustness, cost, and infrastructure dependency, as well as future directions for safe two-wheeler transport, including hybrid sensor fusion, machine learning, and scalable IoT solutions.