Machine learning has started playing a key role in the hydrocarbon and geothermal exploration and production as well as in carbon geo-sequestration. Machine learning has shown to improve the efficiency, efficacy, and productivity of subsurface engineering and characterization efforts. This review paper lays out popular machine learning applications in exploration, extraction, and recovery of subsurface energy resources, primarily in hydrocarbon exploration and production industry, and potentially in geothermal energy and carbon sequestration. There has been rapid increase in sensor deployment, data acquisition, data storage, and data processing for purposes geothermal/fossil energy development and exploration. This has promoted large-scale development and deployment of data-driven methods, machine learning and data analytics workflows to find and extract energy and material resources from the subsurface earth. Subsurface data ranges from nano-scale to kilometer-scale passive as well as active measurements in the form of physical fluid/solid samples, images, 3D scans, time-series data, waveforms, and depth-based multi-modal signals representing various physical phenomena, ranging from transport, chemical, mechanical, electrical, and thermal properties, to name a few. Integration of such varied data sources being acquired at varying scales, rates, resolutions, and volumes mandates robust machine learning methods to better characterize and engineer the subsurface earth.