The study developed a smart helmet prototype that prioritizes delivery rider safety and facilitates logistical communication for small businesses. It is achieved with the smart helmet, utilizing IoT equipped with crash detection and logistics monitoring functions. Various sensors such as accelerometer and alcohol sensors are calibrated to improve accuracy and minimize errors. A mobile he application was introduced to coordinate delivery logistics and track the location of drivers. The system returned 90 percent accuracy in distinguishing from real accidents, and it also had drunk driver detection with an accuracy of 88 percent. An ATTM336H GPS module was used for geolocation tracking, and a mobile application built with Bubble.io and Firebase was integrated into the helmet to send alerts the shop owners of Roger’s Top Silog House who provided delivery drivers as participants for the study, which gave us positive feedback indicating that Smart Helmet performed very well and exceeded expectations.