In the contemporary landscape of computational advertising, measurement has undergone a radical transformation, shifting from a retrospective evaluative function to a continuous, real-time algorithmic apparatus integrated into strategic decision-making. This study employs a systematic literature review methodology, analyzing 40 peer-reviewed articles across high-impact journals to investigate how measurement methodologies have adapted to platform complexities and evolving consumer behaviors. The review categorizes digital marketing measurement across seven distinct technological environments, including social media (Meta/Instagram, Tik Tok), search engine environments, video advertising, e-shops and social commerce. The research highlights a significant methodological divergence between platforms: while short-video requires neurophysiological analysis of visual-audio signals, search environments are evolving toward economic optimization and adaptation to the era of generative search (AI). Beyond traditional KPIs, new research frameworks explore cognitive load, visual attention, and subconscious consumer mechanisms through technologies such as eye-tracking and EEG. Furthermore, the paper identifies a structural gap between academic advancement and business practice; while academia aims for data precision and validity, organizations prioritize utility, speed of execution, and technical constraints. The study’s conclusions emphasize the necessity of “measurement pluralism,” demonstrating that performance evaluation is no longer a post-event report but a multidimensional strategic engine. Integrated success depends on the ability of companies to navigate heterogeneous ecosystems, transforming raw data into active vectors of economic growth.