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Abstract Fog is a critical meteorological phenomenon that significantly impacts transportation safety, and aviation operations. Traditional fog detection methods rely on ground‐based visibility sensors and satellite observations. In addition, the relative humidity is limited to detect fog phases since it reaches saturation just before fog onset and remains constant during fog. This study explores the potential of Global Positioning System (GPS)‐derived integrated water vapour (IWV) as an alternative and complementary approach for fog detection and classification. Using six years (2017–2022) of hourly IWV and surface meteorological data from the Nouasseur region in Morocco, we analyse the temporal evolution of IWV during different fog events, including radiation fog, advection fog, cloud base lowering fog, and advection–radiation fog. The results reveal distinct IWV signatures: a sharp decrease during fog onset due to condensation and dew deposition, stabilization during the mature phase, and an increase during dissipation driven by evaporation and mixing. These patterns vary by fog type, with advection fog showing the most pronounced IWV decline. In addition, these patterns are consistent with a bulk water continuity model, supporting a physically grounded interpretation of fog processess. Seasonal analysis indicates higher fog occurrences in winter and autumn, driven by radiative cooling and moist air advection. Using decision tree classification, based on IWV at onset and its recent variation rates, achieves classification accuracies of 66.7% for non‐fog and 63.9% for fog conditions, highlighting their potential for operational forecasting. However, accuracy decreased for detailed classifications, indicating a need for multivariable approaches. These findings demonstrate that GPS‐derived IWV provides valuable insights into atmospheric moisture dynamics associated with fog formation and offer a pathway for improving fog detection algorithms, with implications for safety and efficiency in fog‐prone regions.