Solar water pumps play a crucial role in sustainable agriculture, particularly in rural regions where reliable access to water and energy is limited. These devices, fueled by renewable solar energy, provide an environmentally sustainable and economical solution for irrigation. However, like any technological system, solar water pumps are susceptible to various faults, including electrical, physical, and environmental issues, which can compromise their efficiency and reliability. This research article examines the utilization of machine learning methodologies to forecast and identify malfunctions in solar water pump systems. This paper examines five machine learning models: Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, and Random Forest—are evaluated for their effectiveness in predicting three key fault types: electrical anomalies, including open circuits; physical faults, including module degradation and cleaning needs; and environmental faults, influenced by factors like wind velocity and temperature. This research study uses sensor data collected from solar water pump systems, including parameters such as current (I), voltage (V), temperature, and solar power. The results show that random forest and logistic regression always achieve the best accuracy in all fault parameters, which is suitable for fault prediction and preventive maintenance. By accurately predicting potential failures, the intelligent system aims to reduce downtime, lower operating costs, and increase the efficiency of solar water pumps. This research paper provides important insights into the optimization of machine learning models for fault prediction in renewable energy systems and advances the overarching objective of fostering sustainability agriculture through technology.