IoT-based sewage monitoring system Real-time measurement and evaluation of hazardous gas levels and medical waste is the goal here. to ensure the security of those working in such dangerous environments. The creation of an Internet of Things (IoT) system that can measure the levels of humidity, temperature, gas mixes, and humidity while simultaneously monitoring the dynamic changes in the aforementioned parameters. If the levels rise above the threshold, the sewage worker will receive a vibration alarm as part of the procedure, and the alert will also be issued to authorized staff who are located remotely and using mobile devices connected to their jobs. Whenever a problem arises, it can be followed up on utilizing notification through the mobile application. The Message Queuing Telemetry Transport protocol is sometimes known as MQTT. This is utilized to send data from our suggested system's sensor to the cloud. Adafruit IO is just one of several excellent services available for data logging or connecting to your microcontroller online. A microcontroller with a Wi-Fi module is called the ESP32.The ESP32 microcontroller is interfaced with sensors such as humidity and gas sensors to measure level and do cloud-based tracking. A fuzzy logic membership function or a built control method improved using a genetic algorithm. Genetic Algorithm optimizations in both decimal and integer form were performed to supply the Genetic Algorithm with a strong sensor threshold value and the current sensor scenario value, which maximizes sensor accuracy and triggers an alarm. Because of the sewage waste, the environment is filthy and a number of diseases are disseminated through the human cycle. Fuzzy logic statements are effective when employed in sewage monitoring. he fuzzy logic is dependent on specific conditions (either true or false). The Central Decision Logic is in charge of keeping track of the method procedure and executing the appropriate algorithm calls (evolving and adaptation mechanisms) (CDL).The evolving mechanisms are calculated based on the conditions for cluster addition, deletion, division, and fusion. The process through which the clusters' centers and fuzzy covariance matrices adapt. The EFUMO approach that has been provided serves as the foundation for the monitoring system. It is in charge of picking up on new information and modifying the fuzzy model that is used to locate sensors and store data transmission in the cloud in order to provide an alarm signal.