The Bay of Bengal (BoB), a critical region within the northern Indian Ocean, experiences complex interactions between aerosols and cloud properties, driven by anthropogenic emissions and natural processes. This study analyzes long-term aerosol and cloud optical properties using coupled Moderate Resolution Imaging Spectroradiometer (MODIS) and Modern Era Retrospective for Research Applications (MERRA-2) datasets with an advanced Artificial Intelligence (AI)-based Machine Learning (ML) model. Seasonal comparisons reveal significant aerosol optical depth (AOD) variability, with peaks during the summer due to dust transport and anthropogenic activities and reductions during monsoons due to wet scavenging effects. The regional patterns highlight elevated AOD and cloud optical depths in the northern BoB, which are linked to industrial emissions and biomass burning, whereas the southern BoB exhibits cleaner marine air, a lower AOD, and larger cloud droplets. Aerosol–cloud interactions are analyzed via spatial correlations, demonstrating how aerosols influence clouds' microphysics, including their optical depth, liquid water path, and precipitation efficiency, with distinct regional disparities. This research provides novel insights into the role of aerosols in modulating cloud characteristics and climate variability in the monsoon-dominated BoB, advancing our predictive capabilities for regional weather and climate systems. It also advances our understanding of aerosol–cloud interactions to improve climate predictions and mitigation strategies while addressing the impacts of air quality on human health and protecting marine ecosystems that are affected by atmospheric processes.