This paper proposes a stacked ensemble framework for path loss prediction in complex urban wireless environments. The approach integrates five base learners-Linear Regression, Random Forest, Decision Tree, Gradient Boosting, and Support Vector Regression-combined using a neural network meta-learner to enhance predictive accuracy. Drive test data from two Nigerian cities were preprocessed and used for model training, with k-fold cross-validation and random search applied for hyperparameter tuning. Evaluation metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R²), confirm that the stacked model improves prediction accuracy by 9.5% over the best-performing base model. The model's generalization was further validated on an independent dataset. These findings demonstrate the advantage of ensemble learning in improving path loss prediction accuracy for 5G network planning.