Effective management of discharged wastewater quality is crucial for maintaining public health, preserving aquatic ecosystems, and ensuring compliance with environmental regulations. However, spatial and temporal data sparsity remains a fundamental constraint. This review critically examines the role of Geographic Information Systems (GIS) and statistical interpolation techniques in bridging these data gaps to create continuous maps of wastewater quality parameters (e.g., BOD₅, COD, TSS, nutrients). Moving beyond a simple compilation of methods, this paper presents a comprehensive framework that categorizes and evaluates interpolation techniques, ranging from deterministic and geostatistical approaches to emerging machine learning (ML) and hybrid models, based on their ability to address specific challenges in wastewater systems. A key contribution is a meta-analysis of 28 comparative studies, which quantitatively synthesizes evidence on the prediction accuracy (RMSE) of different methods. The results indicate that machine learning and hybrid models significantly outperform deterministic and basic geostatistical methods, with a pooled reduction in RMSE of 18.4% (95% CI: 12.1-24.3%) compared to Ordinary Kriging. We explore applications in pollutant tracking, impact assessment, and infrastructure planning, highlighting how the integration of real-time sensor data (IoT) and remote sensing is transforming static maps into dynamic monitoring tools. Finally, we present a forward-looking roadmap for research, informed by our quantitative findings, emphasizing the need for hybrid modeling frameworks that leverage AI, the development of digital twins for wastewater networks, and the integration of uncertainty quantification into decision-support systems. By quantitatively synthesizing the current state-of-the-art and identifying critical knowledge gaps, this review aims to guide future research towards more intelligent, adaptive, and reliable spatial assessments of wastewater quality.