Groundwater is a vital resource, especially in arid regions. However, its quality is increasingly at risk due to contamination from both natural and anthropogenic sources. Therefore, innovative methodologies are needed to develop effective water management strategies. This study assesses groundwater vulnerability in the Maknessy basin through the integration of Remote Sensing Techniques (RST) and Kohonen's Self-Organizing Maps (K-SOM). The aquifer vulnerability index in the study area is classified into three categories: low, moderate, and high, based on essential environmental, hydrogeological, geomorphological, and geological factors. The findings indicate that 27% of the area exhibits high vulnerability, particularly in the central basin and alluvial plains, where favorable local conditions, such as high permeability (1.97 10- 3 m/s), and low slope (0-5), increase the susceptibility of groundwater to contamination. Additionally, 40% of the region falls under moderate vulnerability, influenced by intermediate permeability, land use changes, and variations in drainage patterns. The remaining 33% of the area is categorized as low vulnerability, corresponding to the southern and western mountainous regions, characterized by low slope and impermeable formations. These findings emphasize the need for targeted groundwater management strategies, especially in high and moderate vulnerability zones, to mitigate contamination processes and ensure sustainable land-use practices. Furthermore, the study highlights the necessity of future research studies to identify the effect of climate change on groundwater dynamics, and water isotopic characterization to a deeper understanding of groundwater flow and the identification of contamination risks. The integration of RST and K-SOM proves to be highly effective, providing this research with a robust framework for assessing groundwater vulnerability spatial distribution. This approach supports informed decision-making processes in water resources management, particularly in arid and semi-arid regions and under climate change scenarios.Graphical AbstractThis is a visual summary serves as a pivotal entry point into the research, offering a concise overview of the study's core findings and methodologies for assessing groundwater vulnerability. It begins with "Data Collection," encompassing "Existing Data" such as topography, hydrology, climate, geology, and land use/cover, alongside "Vulnerability parameters" categorized into geomorphology, hydrology, hydrogeology, climate, geology (specifically Water Quality Index - WQI), and vegetation. These data are processed using "Vulnerability methods", specifically a K-SOM (Kohonen Self-Organizing Map) method combined with a MCDM (Multi-Criteria Decision Making) Model, and integrated with "Remote sensing Data." The K-SOM analysis results in a visual clustering (Groups 1 and 2), which, along with individual vulnerability index layers derived from the parameters (Geomorphology index, Hydrology index, Hydrogeology index, Geology index, WQI, Vegetation index, and Climate index), undergoes "Reordering, Scoring, Weighting and Classification". This leads to the "Groundwater vulnerability mapping", ultimately producing a map illustrating the "Spatial variation of Groundwater vulnerability (AVI)" and associated scoring. This detailed method shows why it's so important to combine different kinds of information and smart analysis to accurately understand and map how vulnerable our underground water sources are. This knowledge is vital for managing our water responsibly and keeping it safe from pollution.