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This paper reviewed the application of airborne geophysical and remote sensing datasets in the mapping of orogenic gold deposits in different geologic settings around the world by examining more than forty publications in peer-reviewed journals. The paper indicates the role of aeromagnetic, aeroradiometric datasets (airborne geophysical) and LandSat and ASTER datasets (remote sensing) in mapping epithermal orogenic gold deposits. The paper further highlighted the importance of understanding the geologic settings of epithermal gold mineralization in terms of the mineral system before mineral mapping can be done. Case studies drawn from fourteen (14) publications were presented to show the successful mapping of epithermal gold deposits using airborne geophysical and remote sensing datasets. However, the challenges of the methods of mapping as presented in the paper indicated the limitations of the methods in terms of ambiguity in interpretation, especially when a single method is used. Also, the cost of data acquisition and the inability of the exploration methods to estimate the tonnage and grade of the epithermal gold deposits pose a limitation to the use of airborne geophysical and remote sensing datasets in epithermal gold mapping. The paper was able to justify the use of the methods solely for mapping, which essentially is to focus exploration on certain areas, thereby, saving time and money. Further analyses on tonnage estimation can be done by wildcat drilling and geochemical analysis in mapped areas obtained from airborne geophysical and remote sensing datasets. In addition, the paper presented new technologies that are less expensive than conventional airborne geophysical methods that are capable of probing deep into the subsurface with higher resolution and the use of integrated techniques of airborne geophysical and remote sensing datasets to cater for the ambiguity associated with exploration data interpretation. The paper finally highlights ongoing research in the mapping of epithermal gold deposits in the Ife–Ilesa schist belt involving the development of machine learning algorithms to process voluminous datasets to produce a reliable predictive mineral potential map.