Problems with power management, data accuracy, and system support are becoming more common because of data dependence and the proliferation of automation in sensor networks. The main problems with traditional sensor networks are their short battery life, the necessity for continuous calibration, and the erroneous findings they provide in inconvenient locations. This study shows a new type of sensor architecture that gets calibrated automatically by using energy-gathering technologies along with AI and robotic processing systems. Data processing and calibration algorithms are run in real time by the system's RPA. Solar, thermoelectric, and piezolectric power sources power the sensors separately. By using both RPA and self-powered technologies together, the system can make sensors last longer, require less human input, and self-calibrate to make sure the data is correct. By offering an effective and scalable solution, this new idea opens new ways for autonomous monitoring to be used in important areas such as healthcare, industrial automation, and environmental protection.