Abstract: Mineral exploration is a fundamental task for safeguarding national resource security and the stability of industrial supply chain. As a core step of mineral exploration, mineral prospectivity mapping (MPM) has undergone transformative development, spurred by big data and artificial intelligence, emerging as a prominent research field within Earth science and accumulating a substantial volume of literature. In this study, we employ bibliometric methods to analyze and discuss the research status, developmental trajectory, and hotspot evolution of MPM over the past five decades, based on a dataset of 935 relevant publications from three flagship journals of the International Association for Mathematical Geosciences, spanning from 1969 to 2025. Bibliometric statistics on authors, institutions, and countries reveal that Carranza, E.J.M. and Zuo Renguang are the most highly productive and highly cited scholars in the field as listed author and first/corresponding author, respectively. China is the largest contributor of publications in this field, and the China University of Geosciences (Wuhan) ranks first in global institutions in both publication volume and total citation. The analysis of collaboration networks indicates a strong regional orientation, lacking of a high-level and regular international cooperative research network. The evolution of MPM, based on the hotspot analysis of keywords, is divided into three distinct stages, namely the foundational stage (1969-1990), the expansion stage (1991-2010), and the boom stage (2011-2025). The thematic focus and developmental trajectory of each stage are determined by the prevailing technologies and algorithms of the era. The foundational stage, focusing on mineral resource assessment, was dominated by geostatistics (variogram and Kriging). The rise and widespread application of GIS technology during the expansion stage facilitated the shift of MPM into the mainstream scientific task. In the boom stage, the prevalence of machine learning algorithms led to the dominance of intelligent MPM in the thematic tasks. Recent research hotspots and trends indicate a shift from relying solely on high-performance predictive models towards in-depth exploration and optimization of the internal mechanisms of intelligent models. The focus is on leveraging cutting-edge AI technologies to address inherent challenges such as the black-box nature of decision processes and sample scarcity. Although advanced deep learning algorithms have gained significant traction, classic shallow learning algorithms, such as support vector machine which exhibit great performance in processing high-dimensional data and nonlinear problems, and random forest characterized by its strong resistance to overfitting, remain popular choices among scholars in this field during the boom stage due to their high suitability for few-shot MPM tasks. By leveraging quantitative statistical and visualization tools, this study provides a macro and comprehensive perspective for understanding the development of MPM, and offers critical insights into future research directions of intelligent prediction in MPM.