Optimizing the design and performance of Microstrip Patch Antennas (MPAs) in modern wireless communication systems is highly essential due to increasing demand for compact, multi-band, and wideband antennas. Traditional design methods often fail to provide solutions to the problem; hence, advanced machine learning (ML) techniques have been explored. In this paper, Random Forest, XGBoost, and Gradient Boosting models are used to predict and improve the most significant performance metrics, focusing on S-parameter [dB(S(1,1))]. Among them is Random Forest, which outperformed others with an MSE of 0.0275 and an R-squared score of 0.9990. Its analysis shows that the parameter most important to antenna performance has been operating frequency with a feature importance score of 0.578755, followed by that of feed length and the ground length. These results point toward the promise of ML-driven designs to transform MPA into a future design that captures the new demands of high-speed communication technologies.