Machine learning (ML) has made it much easier to find and estimate the risk of early stage of cardiovascular illnesses by making it possible to analyses massive, various clinical datasets quickly and easily. In these kinds of datasets, demographic information, lifestyle characteristics, medical history, and diagnostic measurements are all included. These are all things that may not be easy to see through standard clinical examination. This study examines heart disease prediction through a series of hybrid ML models that integrate neighborhood-based classifiers, swarm intelligence-driven optimization, and ensemble learning, motivated by existing obstacles. There are four hybrid models being proposed: MSMO-KE and MSMO-KM, which combine Modified Spider Monkey Optimization (MSMO) with K-Nearest Neighbour classifiers that use Euclidean and Minkowski distance measures, respectively. There are also two ensemble variants, MSMO-KECB and MSMO-KMCB, which add CatBoost as a final prediction layer. To make sure it is strong and can be used in other situations, the proposed framework is tested on three separate cardiovascular datasets using a cross-validation method. The experimental findings show that the performance is always better than the baseline and the best models that are already used. The MSMO-KMCB model performs the best overall out of all the approaches tested. It has a cross-validated accuracy of 98.2% on Dataset-3 while keeping a high sensitivity. The comparative research demonstrates that the proposed MSMO-based ensemble models surpass current methodologies in predictive accuracy and recall, underscoring their promise for dependable and efficient heart disease risk prediction in clinical decision-support systems.