With the recent COVID-19 challenges, a growing interest to develop an efficient, economical, and effective infectious waste segregation system has prompted both the health sector and the government. This study presented a Microcontroller-Based Automated Infectious Waste Segregation and Disinfection System in a selected public medical facility in Metro Manila, Philippines. The prototype system applying the machine learning principles is capable of identifying three kinds of waste materials classified as sharps, electronic, and pathological wastes as interpreted by the Phyton Image processing software. In addition, an added feature of UV light mechanism to address the bacterial presence of Staphylococcus aureus and Escherichia coli. was incorporated in the prototype to ensure disinfection. Results showed that the mean average precision (maP) of identification was 95.7, 79.9, and 94.5%, respectively. Moreover, it was found that there was a noticeable decrease in the bacterial count signifying the effectivity of the prototype and has promising potential for large-scale implementation.