Solar energy has a number of concerns, such as dust buildup, environmental changes, and improper tilt of the panel, all of which drag the efficiency of the system. Sensors like temperature, voltage, and current connected with NodeMCU allow constant and accurate tracking of solar systems with invaluable data on energy efficiency for different purposes like agriculture or commercial usage. These systems are improved by ultrasonic sensors that help in the detection of obstacles and distance, although the accuracy of results may be affected by the prevailing conditions. Additionally, microcontrollers like Arduino facilitate autonomous cleaning and dual-axis tracking mechanisms to address issues such as dust layering and panel misalignment, which can drastically reduce power efficiency by up to 50%. Studies also show that these cleaning systems combined with tracking technologies increase that efficiency by 35% by increasing light exposure and decreasing maintenance required. Smart platforms based on Blynk mobile applications offer constant system operation control and thus simplify the process of system management and maintenance. More refined algorithms, such as the smart filters and the least squares methods, also sharpen monitoring accuracy with the ability to predict solar outputs and minimize calculation errors of energy. It seems that the future integration of AI and cloud computing may extend these systems' functions to include predictive maintenance, the use of data collected in a wider range of ways, and increase the systems' potential expandability. IoT-based solar monitoring systems are considered cost-efficient in enhancing solar energy performance, with future developments expected to address issues of network availability, reliability, and costs of maintenance and adaptation to geographical and physical scenarios.