Chlorophyll-a (Chl-a), a proxy for phytoplankton biomass, plays an important indicator in monitoring trophic states of inland waters. This study proposes a comprehensive framework that utilizes two convolutional neural networks (CNNs) for AC (ConvNet-AC) and Chl-a estimation (ConvNet-CHL) in the eutrophic lakes of Hanoi city (Vietnam) using Landsat-8 images. Satellite-based Chl-a retrieval algorithms have been established based on water remote sensing reflectance ($R_{rs}\left(\lambda \right)$). However, existing atmospheric correction (AC) models often struggle to efficiently extract $R_{rs}\left(\lambda \right)$ due to the complex optical properties of turbid lakes, leading to significant errors in Chl-a retrieval. In this study, a total of 45,764 $R_{rs}\left(\lambda \right)$ and 13,561 Chl-a samples are synthesized using radiative transfer AC and regional Chl-a retrieval algorithms to address the scarcity of their data. A two-stage training strategy combined with hyperparameter tuning is utilized to automatically optimize the architecture of both networks. Model validation and testing are performed using a subset of synthesized data and an in situ dataset. In the comparative analysis, numerous AC approaches, including atmospheric correction for OLI “lite”, Case-2 Regional Coast Color, Image Correction for Atmospheric Effects, Landsat-8 Surface Reflectance Code, QUick Atmospheric Correction, and Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH), and the existing regional Chl-a retrieval algorithm are implemented. Results indicate that ConvNet-AC achieves an average $R^2$ = 0.72 and RMSE = 0.0024 sr⁻¹ for $R_{rs}\left(\lambda \right)$ prediction across five spectral bands, outperforming other AC candidates. The ConvNet-CHL achieves $R^2$ = 0.73 and RMSE = 40.40 mg·m⁻³ for Chl-a estimation within a range between 50 mg·m⁻³ and 300 mg·m⁻³, representing a 43% improvement over the existing regional Chl-a retrieval algorithm with RMSE = 71.99 mg·m⁻³. Furthermore, the proposed framework successfully captures the spatial and seasonal patterns of the Chl-a concentration distributions, demonstrating the effectiveness of integrating CNN-based AC and Chl-a retrieval, offering a robust and transferable solution for monitoring inland water quality with limited ground-truth data.