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Machine learning (ML) techniques have been widely applied to the rapid interpretation of transient electromagnetic (TEM) data. However, existing studies primarily focus on using neural networks to directly predict resistivity or its posterior distribution, with limited exploration of extracting more valuable information from network outputs. Additionally, few studies have integrated ML with electromagnetic induced polarization (IP) phenomena for comprehensive analysis. In this study, we investigate the application of ML-based TEM inversion in hydrogeological and mineral resource surveys. For hydrogeological applications, we employ probabilistic neural networks to efficiently estimate the posterior distribution of large-scale FloaTEM data. Furthermore, we extract a laterally constrained smooth model from the posterior distribution and determine the depth of investigation the inversion model. In mineral resource exploration, we assess the feasibility of using machine learning to directly estimate IP parameters from raw airborne TEM data.