Diabetes Mellitus represents a significant global health challenge, characterized by pervasive prevalence and the potential for severe long-term complications. The advent of predictive modeling in the medical field holds promise for early detection and classification of this condition, providing a pivotal advantage for timely and effective intervention. Among various analytical methodologies, ensemble learning techniques stand out for their robustness and accuracy, particularly the Random Forest algorithm, renowned for its proficiency with complex datasets. This paper delves into an empirical comparison between an optimized Random Forest model and an innovative stacking ensemble method, aiming to unravel their efficacies in the context of diabetes prediction. The study leverages a comprehensive diabetes dataset, emphasizing meticulous preprocessing and normalization to ensure data quality. Within this framework, we contrast the performance of a fine-tuned Random Forest classifier against a stacking ensemble model, which amalgamates predictions from diverse algorithms to enhance decision accuracy. Our results underline the nuanced advantages of each approach, with the stacking ensemble demonstrating superior precision and accuracy, albeit with nuanced trade-offs in recall metrics. Through this comparative analysis, we aim to enrich the dialogue within medical informatics, offering insights that could steer future predictive modeling endeavors. By articulating the strengths and synergies of these advanced ensemble methods, the research aspires to contribute substantially to the early detection and management strategies of Diabetes Mellitus, fostering improved patient prognoses and healthcare outcomes.