The domain of business-to-business (B2B) relationship marketing has completed four decades, which is a significant milestone in any field’s life cycle. At this juncture, a thorough integrative review is required to consolidate the scattered knowledge toward gaining a comprehensive understanding of the field. Our study aims to address this requirement, which will aid in enhancing the domain’s vitality and future growth. To achieve this objective, we adopted a machine learningbased text analytics method using structural topic modeling (STM), tm for text preprocessing, and ggplot2 for data visualization using R-software to extract, explore, and map the key themes and their evolution from the extant research on B2B relationship marketing literature for the 40-year period from 1983 to 2022.Using structural topic modeling (STM), we identified 14 topics from B2B relationship marketing literature, out of which five are trending (namely, social media and network relationships, B2B relationship selling, CRM, B2B service relationships, and B2B relationship performance), suggesting high future fertility of these research areas. Furthermore, we developed a comprehensive organizing framework based on the STM results. We end with implications for theory and future research.