Multi-touch attribution (MTA) has emerged as an imperative analytical framework in marketing analytics, driving the strategic optimization process through accurate attribution insights in the context of the widening complexities of digital consumer behavior. The following bibliometric analysis explores research relevant to MTA, including patents, theses, conference proceedings, and journal articles published from 2010 to 2024, to present an outlook on significant development, critical methodologies, and shifting trends. Conclusions point to a radical shift from the rule-based traditional approaches to more AI-enhanced data-driven methods by machine learning and sophisticated statistical techniques that will continue to enhance predictive accuracy. Further, blockchain technologies have also been underlined among factors enhancing data privacy in MTA systems. Prominent contributors include Kannan, Li, and Pauwels. Publicly available datasets, such as those published by Criteo, have catalyzed immense methodological innovation and provided empirical evidence for model development. Despite these many works, several challenges remain in model interpretability, data quality, and privacy considerations within MTA applications. Future research will likely concentrate on federated learning, privacy-preserving analytics, and real-time data processing, enabling further improvements to the MTA framework. This research combines updated information about MTA, serving as primary material for scholars in delimiting potential ways to improve the methodology of MTA in digital marketing.