Machine Learning Frameworks for Media Consumption Intelligence across OTT and Television Ecosystems

The rapid expansion of digital media ecosystems most prominently Over-The-Top (OTT) streaming platforms has fundamentally transformed audience behavior, content discovery paradigms, and consumption intelligence models. Traditional television ecosystems have relied for decades on panel-based audience measurement frameworks, but the growing ubiquity of connected devices, cloud-based distribution, and personalized recommendation engines necessitates the introduction of sophisticated machine learning (ML) approaches. Machine learning frameworks enable content providers, broadcasters, and advertisers to analyze heterogeneous data sources at scale—including viewership logs, device metadata, user demographic profiles, contextual signals, and multimodal content attributes—to construct unified intelligence layers for media consumption prediction and optimization. This paper presents a comprehensive examination of emerging ML-driven architectures for media consumption intelligence, emphasizing unified modeling across hybrid environments consisting of both legacy broadcast television and modern OTT ecosystems. Unlike traditional analytics approaches that treat linear TV and digital streaming