In today’s fragmented digital marketing landscape, understanding the true impact of marketing efforts across multiple platforms is an increasingly complex yet essential challenge. As consumers interact with brands across various channels—social media, email, search engines, websites, and mobile apps—traditional last-touch or heuristic-based attribution models fail to capture the nuanced contributions of each touchpoint. This has created a pressing need for more accurate and theoretically grounded attribution models capable of evaluating cross-platform marketing performance holistically. This paper conducts a comprehensive literature-based investigation into the evolution, limitations, and innovations in marketing attribution modeling. Emphasizing a conceptual perspective, it synthesizes existing frameworks, analytical methods, and empirical findings to propose a unified attribution model suitable for dynamic, multichannel environments. The study explores rule-based, statistical, algorithmic, and hybrid approaches, assessing their effectiveness in tracing customer journeys and allocating credit to influencing interactions. Furthermore, it examines the role of data granularity, privacy regulations, and real-time processing in shaping attribution models. The proposed conceptual model seeks to bridge gaps in accuracy, interpretability, and strategic utility, offering a roadmap for marketers, analysts, and researchers to optimize performance evaluation across platforms. By integrating theoretical rigor with practical relevance, the paper contributes to the advancement of attribution science in the digital marketing ecosystem.