As the world increasingly prioritizes sustainable energy solutions, there is an urgent need for more efficient, reliable, and scalable systems. This study is motivated by the critical need to address inefficiencies and enhance the reliability of renewable energy systems (RES). Integrating artificial intelligence (AI) with RES and the Internet of Things (IoT) offers a promising for meeting these demands. This study investigates the role of IoT in conjunction with AI in renewable energy management, focusing on the challenges and opportunities presented by their integration. The paper explores the integration of AI models; specifically multi-layer perceptron (MLP), support vector machine (SVM), decision tree (DT), random forest (RF), gradient boosting (GB), and category boosting (CB) with IoT-enabled RES. The key challenges addressed include data quality, real-time processing, and system scalability. The role of IoT in maintaining data accuracy and consistency is crucial for the performance of these AI models. Additionally, IoT infrastructure supports the scalability of RES by allowing for the seamless addition of new sensors and devices, thus facilitating the expansion and enhancement of AI capabilities. Our study provides a detailed analysis of various AI models based on performance metrics such as R-square, mean absolute error (MAE), explained variance (EV), and Pearson correlation coefficient. The results reveal that the RF model excels, achieving an R-square of 0.99956, MAE of 4.82684, EV of 0.99956, and a Pearson correlation of 0.99978, demonstrating its superior capability in managing and optimizing renewable energy data within an IoT framework.