Geothermal is a renewable energy source that can provide reliable and flexible electricity generation for the world. In the past decade, play fairway analysis (PFA) studies identified that geothermal resources without surface expression (e.g., blind/hidden hydrothermal systems) have vast potential. However, a comprehensive search for these blind systems can be time-consuming, expensive, and resource-intensive, with a low probability of success. Accelerated discovery of these blind resources is needed with growing energy needs and higher chances of exploration success. Recent advances in machine learning (ML) have shown promise in shortening the timeline for this discovery. This paper presents a novel ML-based methodology for geothermal exploration towards PFA applications. Our methodology is provided through our open-source ML framework, GeoThermalCloud https://github.com/SmartTensors/GeoThermalCloud.jl. The GeoThermalCloud uses a series of un-supervised, supervised, and physics-informed ML methods available in SmartTensors AI platform https://github.com/SmartTensors. Through GeoThermalCloud, we can identify hidden patterns in the geothermal field data needed to discover blind systems efficiently. Crucial geothermal signatures often overlooked in traditional PFA are extracted using the GeoThermalCloud and analyzed by the subject matter experts to provide ML-enhanced PFA (ePFA), which is informative for efficient exploration. We applied our ML methodology to various open-source geothermal datasets within the U.S. (some of these are collected by past PFA work). The results provide valuable insights into resource types within those regions. This ML-enhanced workflow makes the GeoThermalCloud attractive for the geothermal community to improve existing datasets and extract valuable information often unnoticed during geothermal exploration.