The study meticulously explores the cutting-edge domain of spectral remote sensing technologies tailored for the nuanced monitoring of water quality across diverse aquatic ecosystems. It places a strong emphasis on the innovative integration of Convolutional Neural Networks (CNNs) and groundbreaking spatio-temporal-spectral fusion models, setting a new precedent in environmental data analytics. Through a rigorous examination, we unveil the unparalleled efficacy of spectral remote sensing in unraveling the complexities inherent in key water quality parameters, including but not limited to, the concentration of phytoplankton pigments and fluctuating salinity levels. This is achieved via the strategic deployment of sophisticated computational algorithms that dissect and interpret the intricate data derived from spectral signals. Our discourse extends to illuminate the transformative impact of satellite-based remote sensing, revolutionized by the introduction of high-resolution spectral imaging coupled with the prowess of machine learning techniques. Such advancements facilitate not only the precision but also the expansiveness of water quality assessments, encompassing vast geographical terrains with remarkable accuracy. Through a methodical comparative analysis of various inversion models, the paper delineates the subtle yet powerful capabilities of these methodologies in extracting and decoding accurate environmental data from the complex interplay of spectral signatures. Moreover, the research accentuates the critical importance of hybrid analytical models that seamlessly blend spatial, temporal, and spectral data streams. This holistic approach furnishes a more intricate and dynamic understanding of aquatic ecosystems, enabling stakeholders to navigate and manage the nuances of these environments with greater efficacy. The synthesis of avant-garde remote sensing technologies with advanced computational models encapsulated in this study not only signifies a pivotal advancement in the realm of environmental monitoring but also lays down a robust framework poised to catalyze future innovations in the sustainable stewardship of water resources.