The threat of snakebites to public health, particularly in tropical and subtropical regions, requires effective mitigation strategies to avoid human-snake interactions. With the development of an IoT-based smart snake-trapping device, an innovative non-invasive solution for preventing snakebites is presented, autonomously capturing and identifying snakes. Using artificial intelligence (AI) and Internet of Things (IoT) technologies, the entire system is designed to improve the safety and efficiency of snake capture, both in rural and urban areas. A camera and sensors are installed in the device to detect heat and vibration signatures, mimicking the natural prey of snakes using tungsten wire and vibration motors to attract them into the trap. A real-time classification algorithm based on deep learning determines whether a snake is venomous or non-venomous as soon as the device detects it. This algorithm utilizes a transfer learning approach using a convolutional neural network (CNN) and has been trained using snake images, achieving an accuracy of 91.3%. As a result of this identification process, appropriate actions are taken, such as alerting authorities or releasing non-venomous snakes into the environment in a safe manner. Through the integration of IoT technology, users can receive real-time notifications and data regarding the trap via a smartphone application. The system's connectivity allows for timely intervention in case of venomous species, reducing snakebite risks. Additionally, the system provides information regarding snake movement patterns and species distribution, contributing to the study of broader ecological issues. An automated and efficient method of managing snakes could be implemented in snakebite-prone regions with the smart trapping device.