<p>Marketing measurement faces a triple disruption: privacy regulations restrict user-level data, artificial intelligence transforms modeling capabilities, and large-scale field experiments expose systematic biases in platform-reported advertising effectiveness. This systematic review synthesizes 108 studies (2010–2026) on how AI and machine learning reshape measurement across multi-touch attribution, media mix modeling, causal calibration, and privacy-preserving measurement. We develop a two-dimensional taxonomy mapping AI techniques to measurement functions and propose a “Measure–Validate–Protect” (MVP) framework that adapts NIST AI trustworthiness dimensions to marketing contexts. Three findings emerge. First, deep learning attribution improves predictive accuracy but remains causally unidentified without experimental calibration. Second, neural media mix models can outperform traditional Bayesian frameworks in selected benchmark and production settings, yet causal credibility depends on external validation and generalization remains under-tested. Third, privacy-preserving techniques impose measurable accuracy costs: differential privacy reduces precision by 2–33% depending on privacy budget, while federated approaches have retained roughly 90–95% of centralized performance in documented deployments. We identify gaps at the intersection of causal validity and privacy compliance and propose a research agenda for trustworthy marketing AI.</p>