Digital advertising cost and efficacy are affected by false click detection. Bot, substandard, or phony clicks cost advertisers money by masking performance. This study uses AI to detect and filter incorrect clicks in real time. The suggested solution evaluates session attributes, user activity, and traffic anomalies using machine learning and deep learning. IP activity, click frequency, device fingerprinting, and temporal trends yield features. Supervised and unsupervised models detect user fraud. System responds to new attacks through iterative learning. Accuracy is much greater than rule-based detection. The strategy improves fraud detection and decreases false positives. Effective large ad platform data processing pipelines offer scalability. Openness and reliability improve digital advertising ecosystem confidence.