Multi-touch attribution (MTA) modeling has become increasingly vital in digital marketing for accurately distributing credit across multiple customer touchpoints, leading to conversions. This study systematically reviews the literature on original research articles on MTA modeling published between 2011 and 2024. Using Zotero for data gathering and organization, we first gathered an initial dataset of 345 works, which was then refined through manual selection and strict inclusion criteria to a final corpus of 62 original empirical research comprising 29 conference papers and 33 journal articles. Mapping the publication trends revealed dramatic swings in volume over time; changes in levels of research output in different forms also marked shifts in emphasis and maturation. Influential authors, such as P.K. Kannan and H. Alice Li, were then identified using CpY. Identification included major journals and leading conference proceedings because MTA is also naturally interdisciplinary across the different boundaries of marketing, data science, and analytics. Such improvements included the thematic categorization of moving from heuristic to data-driven models, the use of game theory and Shapley value for distributing fair credit, machine learning/deep learning techniques, state-of-the-art causal inference/bias mitigation, investigating time-varying effects: survival analysis, scalability considerations and real-time bidding, offline and omnichannel data integration, model interpretation/explainability enhancement, advanced modeling to optimize marketing strategy, and modeling complex consumer journey challenges in high involvement/B2B settings. Therefore, this review presents the current trend, essential contributions, and future directions of MTA research to academics and practitioners.