<p>Sustainable water resource management requires regular water quality monitoring, especially in urban lakes that are prone to contamination. Total Suspended solids (TSS), are the crucial water quality indicator, can now be evaluated in a new and cost-effective way thanks to multispectral remote sensing and machine learning techniques. The reflectance data in the Green, Red, Red Edge, and Near-Infrared (NIR) bands were recorded using a Parrot Sequoia multi-spectral camera mounted on an Unmanned Aerial Vehicle (UAV). The multi-spectral data was analysed using a set of computer programs, including Pix4D Mapper, QGIS, and SAGA GIS, in order to produce reflectance maps and determine the Turbidity Index. A case study is conducted on the Pipliyahana Lake in Indore, which is located in the Indian state of Madhya Pradesh. In addition to the UAV data gathering, water samples from the lake have been taken at certain locations. The Madhya Pradesh Pollution Control Board's (MPPCB) laboratory performed standard testing on the samples for both water quality indicators. Equations for the TSS were created by correlating these findings with the data from the UAV survey. According to the results, the multispectral method obtained an accuracy of 93.65% for Total Suspended Solids (TSS). UAV-based data collection is proven to be more cost-effective and time-efficient than traditional sampling and laboratory analysis. The method’s wide geographic coverage allowed for the identification of pollution hot spots around the lake, something that point-based sampling frequently overlooks. This study shows the ground-breaking potential of integrating remote sensing and AI-driven analysis for real-time water quality monitoring. In addition to being economical and scalable. The approach used in this study offers a practical means of regularly monitoring urban water bodies. Future applications of this technology could enhance water resource management, impact policy decisions, and protect aquatic habitats in rapidly urbanizing places.</p>