As online marketing continues to gain prominence, accurately predicting user behavior remains a significant challenge due to the complexity of human actions. To enhance advertisement click predictions and, by extension, digital marketing strategies, this study utilizes advanced machine learning (ML) techniques. The dataset undergoes preprocessing steps, including handling missing values, feature scaling, and one-hot encoding. Several classifiers, including Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), XGBoost, LightGBM, Artificial Neural Networks (ANN), and Recurrent Neural Networks (RNN), are evaluated. An ensemble model combining LR, RF, and SVM is developed, achieving superior performance with an accuracy of 98% and an AUC of 0.99. This research contributes to advancing intelligent ad engagement prediction, providing advertisers with a powerful tool to maximize impact and efficiency in the digital marketplace.