In the evolving landscape of enterprise oversight, traditional corporate governance systems are increasingly challenged by the complexity and dynamism of modern regulatory, ethical, and operational environments.This paper presents a novel, integrated framework for optimizing corporate governance decisions using machine learning, business process modeling, and explainable artificial intelligence.Data collected from three multinational organizations in Ghana and Nigeria were used to train and evaluate three supervised ML models, Decision Tree, Random Forest, and Neural Network, on governance decision classification tasks.Preprocessing involved imputation, normalization, and stratified data splitting (80/20), followed by model training and performance evaluation using precision, recall, F1-score, and AUC metrics.Experimental results showed that the Random Forest model consistently outperformed others, achieving an AUC of 0.94, precision of 0.91, recall of 0.89, and