Student placement is a critical performance indicator for higher education institutions, reflecting the effectiveness of academic programs and employability initiatives. Accurate prediction of student placement outcomes can help institutions identify at-risk students and implement targeted interventions to improve employability. Traditional prediction approaches often focus solely on predictive accuracy while overlooking the interpretability of the models. This study proposes an Explainable Ensemble Learning Framework for predicting student placement outcomes in higher education. The framework integrates Random Forest, Gradient Boosting, and Extreme Gradient Boosting (XGBoost) classifiers to enhance prediction performance while employing SHAP (SHapley Additive exPlanations) for model interpretability. The proposed model utilizes academic, demographic, and skill-based attributes to predict whether a student will secure placement. Experimental results demonstrate that the ensemble model achieves superior accuracy compared to individual classifiers, with an accuracy of 91.8%, precision of 90.6%, recall of 92.4%, and F1-score of 91.5%. SHAP analysis reveals that academic performance, communication skills, internship experience, and aptitude test scores are the most influential factors affecting placement outcomes. The proposed framework provides both predictive capability and transparency, enabling educational institutions to make informed decisions and improve student employability.