This study focuses on the implementation of a bibliometric analysis of the research concentrated on the detection of breast cancer using AI-based techniques from 2016 to 2023. It reveals the trends, dynamics, and other scientific outputs accumulated during the study, dissecting the 801 most cited articles in breast cancer research. Our study introduces a novel and holistic dimension to establish the most discrete publications within the area of breast cancer research. The bibliometric analysis was performed using the bibliometric and visualization analysis packages such as Bibliometrix available in R studio. A query using the keywords “breast cancer,” “diagnosis,” “prognosis,” “deep learning,” and “machine learning” was launched in the search fields of two reliable databases, namely, Web of Science and Scopus, and consequently, an aggregate of research papers was procured for review, expanding over 8 years. The results derived from the bibliometric analysis have been compartmentalized into two categories based on the number of records contained in the dataset obtained from the two databases with redundancies removed. The query used to acquire the relevant research papers has been the same, consolidating similar keywords with appropriate constraints. Automated diagnostic studies of breast cancer based on the results realized from these 801 papers accessed from the Web of Science and Scopus remain relevant to date. Conclusively, this area of research holds immense scope for further investigation to obtain deeper insights and inherently motivates and leverages the associated researchers.