River water's concentration of chlorophyll-a is a crucial sign of the productivity and health of aquatic ecosystems. There are restrictions on the amount of space and time that can be covered by using labor-intensive field sampling and laboratory analysis in traditional methods of determining chlorophyll-a levels. In this work, we suggest a novel method to improve the evaluation of chlorophyll-a in river water by combining deep learning techniques with data from remote sensing. Our approach entails the collection of multispectral satellite imagery and the creation of artificial neural network (ANN) models that are informed by ground truth measurements of chlorophyll-a [1, 3]. By conducting extensive experiments on datasets collected from different river systems, we show the effectiveness of our approach. According to the results, when compared to traditional methods, our deep learning models predict chlorophyll-a concentrations with superior accuracy. Moreover, spatially extensive and temporally frequent monitoring are made possible by the use of remote sensing data, which makes it easier to identify changes in chlorophyll-a levels quickly [1, 2]. Our research demonstrates how deep learning and remote sensing technologies have the potential to completely transform aquatic ecosystem monitoring and management, opening the door to more successful conservation tactics and sustainable resource use.