One of the common and fatal forms of cancer among people of all age groups all over the world is breast cancer. This condition affects millions of people every year. Early and precise detection of the disease is the most important step towards improving the chances of being cured and surviving longer. Machine Learning is a new and changing approach in medical diagnostics that allows for precise and automatic classification of its hyperparameters. The research has been carried out by combining Bayesian optimization, grid search, and stacked generalization to enhance breast cancer classification. Methodology includes steps of data preprocessing, feature selection, and optimized hyperparameter tuning to raise classification accuracy. A baseline model was developed using Random Forest classifiers without any hyperparameters resulting in the accuracy of 94.74%. Further, the model applies Bayesian Optimization to search hyperparameters, which Grid Search supervised in detail. Thus, a Stacked Ensemble Model with meta-classifier refining predictions was constructed, using multiple optimized Random Forest classifiers. The novelty of this approach lies in its hybrid optimization strategy, which synergistically combines the broad exploration of Grid Search with the efficient fine-tuning of Bayesian Optimization, further enhanced by a stacked ensemble architecture. This unique combination overcomes the limitations of individual optimization techniques and leads to a significant performance advantage. Having applied such an approach, the proposed framework has an increased accuracy of 97.36% in terms of breast cancer classification. This research provides a formalized framework to optimize ML models to be used in medical diagnoses to ensure a more reliable and scalable detection strategy. The study results contribute to the development of robust, data-driven solutions toward automated breast cancer diagnosis providing a hopeful avenue for future research and clinical work.