Wetlands are vital ecosystems with significant ecological, cultural, and socioeconomic importance. However, in wet-dry tropics, they face immense pressure from climate change and human activities. Northern Australia, particularly the Northern Territory, hosts pristine rainfed, river-fed, and groundwater-fed wetlands. Climate models predict increased rainfall and temperature in this region in the future. Despite their importance, these wetlands remain understudied due to their complexity, vastness, and remoteness, with the last mapping update in 2005. This study quantifies and illustrates the dynamics of wetlands in the Daly River catchment, Northern Territory, using Random Forest, a robust Machine Learning model, and Traditional Ecological Knowledge (TEK). Time-series changes from 2015 to 2024 were analysed using Sentinel-2 optical imagery and Sentinel-1 (SAR) imagery with 10 m resolution. Water year imagery (September 1 to August 31) was processed to calculate percentiles (10%, 25%, 50%, 75%, 90%, maximum, minimum) for cloud-free composite images and 50% (median) SAR composite images. Water, soil, vegetation, and topographic indices such as HAND, slope, and elevation were used, as well as TEK of wetland dynamics from Indigenous communities. The results reveal significant changes in wetland coverage, with seven primary wetland types: River water, Non-Forested Swamp, Forested Swamp, Seasonal Marshes, Floodplains, Mangroves, and Riparian Vegetation, increasing by 16% since 2015. These changes are likely attributed to climatic variables, i.e., rainfall pattern and intensity. The wetland maps were validated using fieldwork samples and TEK, achieving accuracies of 78% (Kappa 0.76) for 2015-2016, 81% (Kappa 0.78) for 2019-2020, and 84% (Kappa 0.82) for 2023-2024. By integrating advanced mapping algorithms with TEK, this study emphasises the critical role of Indigenous Knowledge in understanding wetland dynamics and utilises it as a resource for Indigenous communities, policymakers, conservationists, and researchers. The approaches and findings of this study not only reflect wetland changes in tropical regions worldwide but also highlight the significance of TEK in understanding wetland extent.