This study presents a novel ‘Optimized Parallelized Ensemble Learning’ (OPEL) theory, a parallelized multi-mode ensemble learning framework that optimize computational efficiency, speed and model accuracy. This is accomplished by formulating theoretical mathematical models that guide model selection, weighting, and parallel execution strategies and utilizing performance metrics like the Matthews correlation coefficient to select top-performing models. Experimental simulations on real-world datasets demonstrated significant reductions in computation time and improvements in model accuracy compared to conventional ensemble methods. A paired t-test confirms the statistical significance of these improvements, highlighting OPEL’s potential in distributed and resource-constrained environments. This paper henceforth introduced key innovations, which include the Parallelized Model Execution (PME) approach, Consensus-Based Model Selection (CMS), and Optimized Parallel Voting Mechanism (OPVM), each contributing to reduced computational time and improved model performance. The study demonstrates significant gains in computational speed and accuracy through parallelization and advanced voting techniques, with a time complexity reduction as defined by Amdahl's Law. The proposed ensemble learning framework is validated as both computationally efficient and robust in diverse, large-scale AI applications.