Groundwater-dependent invasive alien species (GDIAS) pose significant threats to biodiversity, ecosystem integrity, and groundwater resources globally. Field-based methods for detecting GDIAS, although accurate, are labour-intensive and often lack sufficient spatial and temporal coverage. Recent advancements in satellite-remote sensing (RS), cloud computing, and machine learning (ML) offer promising solutions to these challenges. This systematic review serves as the first review to examine the progress in these innovative technologies for delineating GDIAS and highlights existing knowledge gaps. Furthermore, the review elucidates the fundamental principles and methodologies of RS techniques currently employed for the rapid detection and delineation of groundwater-dependent ecosystems (GDEs) and invasive alien species (IAS). Based on the systematic review of 668 articles, only 6% (n = 40) focused on GDIAS, while 69.4% (n = 464) and 24.6% (n = 164) focused on IAS and GDEs over 24 years. The results revealed a strong research imbalance, where IAS and GDEs have been studied extensively and separately, while GDIAS remains largely overlooked. Linear trend analysis further revealed a strong increase in RSand ML-based research for IAS and GDEs (R-2 >= 0.68, p < 0.0001), but only weak emerging growth for GDIAS (R-2 = 0.23), highlighting a significant methodological and thematic gap. Addressing this gap is critical for improving early detection, informing management strategies and advancing GDEs conservation. Ultimately, this study enhances our understanding of these advanced methodologies, which are crucial for ecosystem protection, invasive species management, and water resource conservation.