This research mainly contributed to the investigation of the possibility of applying machine learning and IoT technologies to predict solar energy potential and optimize households’ electricity consumption. A 10 kW solar panel system of capacity was installed in a residential setting. Over 120 days of data collection were performed with energy meters, PV meters, current, voltage, panel temperature, and atmospheric humidity sensors. The complete record for this dataset was utilized to train and evaluate several machine learning models, which include Decision Trees (DT), Random Forests (RF), Artificial Neural Networks (ANN), and Support Vector Machines (SVM). The performance was evaluated based on accuracy, precision, recall, and F1-score; hence, the SVM model was recorded as being effective enough, coming up with an impressive accuracy of 98.34%. The model performed much better in predicting real-time scenarios, which greatly enhanced the optimization of household usage of energy with accurate and detailed available information on solar power and made concomitant adjustments based on these details. There was a massive reduction in reliance on sourced energy in real time, leading to optimization in terms of cost and overall efficiency in energy usage. Thus, the results underscore the possibility of conducting intelligent energy management along the lines of IoT, where machine learning algorithms can make data-intensive computationally apparent the possibility of developing sustainable and cost-effective residential energy optimization.