Our capstone project aims at applying advanced machine learning techniques to identify advertisement traffic from the cellular network traffic. The idea behind classifying advertising traffic is relevant in today’s business era where digital marketing has become the predominant medium to increase outreach and connect directly with customers. Our team developed a novel approach to label & predict this advertisement traffic (accuracy ~97%), thereby paving the way for further research into identifying and blocking malicious/spam advertisements. The relevant activities we performed included getting data from network captures, data pre-processing, feature extraction, ad-labeling, applying ML algorithms and evaluating model performance.