The integration of renewable energy into modern power systems necessitates effective prediction and management strategies, particularly for solar power, which is characterized by its variability and intermittency. This paper investigates the use of machine learning models to predict solar power generation and enhance power management within virtual power plants (VPPs). The study encompasses data preprocessing techniques, feature engineering, and the application of multiple machine learning algorithms, including Linear Regression, Decision Trees, Random Forest, Extra Trees Regressor, and K-Nearest Neighbors (KNNs). Key meteorological and operational factors influencing solar energy output are analyzed to build predictive models that can optimize resource allocation and energy dispatch in VPPs. The study also addresses the importance of handling data quality issues, such as missing values and feature scaling, to ensure accurate model training and evaluation. Additionally, the paper outlines a future framework for integrating advanced energy storage systems, such as electric vehicles (EVs), and implementing bidirectional grid interaction for resilient energy management. By focusing on machine learning’s role in forecasting and optimization, the study aims to provide actionable methodologies to improve the sustainability and reliability of VPP operations. This work contributes to the ongoing development of smart energy systems by addressing the challenges of renewable energy integration through data-driven approaches.