Solar panel technology has seen significant advancements in recent years, establishing it as the most preferred renewable energy source. However, the performance of solar panels can be affected by various environmental factors, including dust, temperature, humidity, and solar radiation intensity. Reduced solar panel output not only decreases system efficiency but also accelerates module degradation. To address this challenge, the implementation of the Internet of Things (IoT), which enables real-time monitoring, has emerged as an effective solution. Furthermore, fuzzy logic methods can be utilized to monitor the real-time performance of solar panels based on voltage and current inputs. This approach is particularly advantageous because it can handle uncertainty and ambiguity in input data, thereby providing more accurate results. The outcomes of this research include the development of a tool for detecting and monitoring current and voltage from solar panels, accessible via a website. The solar panel output data ranges from 5 to 383 V and 0 to 10 A, reflecting variations corresponding to sunlight intensity. Using the Mamdani fuzzy logic method and defuzzification via the Weighted Average technique, real-time crisp values are displayed. For instance, a performance value of 16.6% indicates poor performance, while a value of 82.2% reflects optimal performance.