Lahore, Pakistan's second-largest city, faces significant public health risks due to groundwater contamination in its over-exploited aquifers, which serve as the primary water source for municipal and domestic use. This study introduces a novel, integrated approach to assess groundwater quality across Lahore district, combining geospatial analysis, geostatistics, and multi-criteria evaluation of water quality indices. The research aims to identify spatial patterns of key contaminants, assess their impact on water potability, and evaluate potential remediation strategies. The methodology integrates Geographic Information Systems (GIS) with Multi-Criteria Decision Analysis (MCDA) and water quality indexing. Expert-driven Analytic Hierarchy Process (AHP) was employed to assign weights to various water quality parameters, with arsenic receiving the highest priority (weight 0.28), followed by total dissolved solids (0.22) and hardness (0.15). The GIS-based weighted overlay analysis revealed critical quality hotspots, particularly in urban-industrial areas such as Lahore Cantt, Model Town, and parts of Lahore City, where water quality index values exceeded 150, indicating very poor quality. These findings align with reports of uncontrolled industrial effluent discharge contributing to aquifer contamination. Scenario modeling demonstrated that a 30% reduction in heavy metals, particularly arsenic, could improve water quality indices by up to 20.71% in severely affected areas like Shalimar. Simulations of advanced water treatment processes indicated potential arsenic reduction exceeding 95%, highlighting the need for sophisticated oxidation and filtration infrastructure. This integrated approach offers a powerful decision support tool for visualizing complex contamination patterns, assessing remediation options, and prioritizing risk-mitigation investments. It provides urban planners and water management authorities with crucial insights for developing targeted groundwater quality restoration strategies, including the strategic placement of treatment facilities, improvements in drainage infrastructure, and the implementation of stricter pollutant discharge regulations. The framework's adaptability makes it applicable to other regions facing similar groundwater contamination challenges, offering a robust methodology for data synthesis and quantitative scenario modeling to address widespread water quality issues.