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The accurate prediction of demographic attributes of users, including age and gender, is a pivotal challenge in personalized search, ad targeting, and other related fields. This information enables companies to refine their target audience and enhance the overall user experience and quality of service (QoS). Among these, the Saudi Telecommunication Company (STC), a premier telecommunications provider in Saudi Arabia, Middle East, and Africa, recognizes the substantial role of age-prediction systems. This study, therefore, explores the application of machine learning (ML) techniques to predict user age, thus assisting in the delivery of age-appropriate ads and offers. We utilized a dataset provided by STC, comprising three million samples with key user and device features. Four ML algorithms were employed in this analysis: the Artificial Neural Network (ANN), Random Forest (RF), Gradient Boosting (GB), and …