Halophila stipulacea, a small-leaved, fast-spreading seagrass, dominates subtidal meadows in the northern Gulf of Aqaba (GoA), where its distribution is affected by seasonal flash floods and climate-related stressors. Accurate monitoring of such meadows remains challenging due to their fine-scale structure and growth in optically complex, turbid waters. Traditional field-based mapping is logistically limited in scope, while many remote sensing approaches underperform in deeper or noisy marine environments. In this study, we present an AI-powered, reproducible workflow for subtidal seagrass mapping, integrating multi-source satellite reflectance data (VENµS and Sentinel-2) with field-validated machine learning models. Five regression algorithms (RT, RF, GBRT, SVR, and XGBR) were trained and tested using in situ data and satellite-derived spectral inputs, including raw bands and vegetation indices. XGBR models trained on VENµS imagery outperformed all others (R2 = 0.97; RMSE = 0.21), demonstrating strong predictive performance even in dynamic coastal zones. We further examined the influence of episodic disturbances such as floods on spatial patterns of vegetation loss and regrowth. Beyond performance benchmarking, the workflow contributes to ecological informatics by producing spatially explicit, scalable predictions designed with transparency and interoperability in mind. The pipeline supports standardized data ingestion, flexible ML configuration, and modular visualization of outputs, enabling integration into digital libraries, semantic search tools, and spatial decision-support systems. This work illustrates how combining remote sensing, structured ecological data, and AI-based inference can improve knowledge synthesis in marine ecology. It offers a transferable methodology for monitoring invasive species, supporting conservation planning, and evaluating ecosystem resilience under climate-driven pressures.