This study presents a multi-methodological assessment of groundwater vulnerability in the Ain Oussera Plain, Algeria, utilizing three DRASTIC model approaches integrated with machine learning techniques. The study analyzed an area of approximately 3795 km2 using standard DRASTIC, modified DRASTIC and DRASTIC-LULC models, validated through field measurements. The standard DRASTIC model revealed vulnerability indices ranging from 88 to 137, with 88.50 % of the area showing low vulnerability. The modified DRASTIC approach (90-141) demonstrated a shift toward higher vulnerability classifications, with moderate vulnerability zones increasing to 12.35 %. The DRASTIC-LULC model, incorporating land use factors, generated indices from 95 to 177 and uniquely identified high vulnerability zones (3.65 %) while showing a significant redistribution of vulnerability classes. Sensitivity analysis identified the impact of the vadose zone as the most important parameter (34.2 % effective weight). Model validation through nitrate (0-78.5 mg/L) and TDS (619.98-2833.62 mg/L) measurements demonstrated the superior performance of the DRASTIC-LULC model, with correlation coefficients of 0.56 and 0.75. The integration of Random Forest classification for land use mapping achieved 98.89 % accuracy, significantly outperforming traditional methods. The results provide crucial insights into groundwater protection strategies in semi-arid regions, emphasizing the importance of incorporating land-use factors in vulnerability assessments and establishing comprehensive monitoring programs.