We present a customized convolutional neural network (CNN) architecture, implemented in the MATLAB environment, designed to simultaneously predict optically active (Total Organic Carbon, TOC) and non-optically active (Dissolved Oxygen, DO) parameters from eighteen Sentinel-2 Level-2A satellite images. Our approach integrates spatial and spectral data through a customized CNN with three convolutional layers and two dense layers, optimized via adaptive learning strategies, data augmentation, and rigorous regularization to enhance predictive performance and prevent overfitting. The models were trained and validated on fused datasets of satellite imagery and in-situ measurements, organized into comprehensive four-dimensional arrays capturing spectral, spatial, and sample dimensions. Our contribution, accessible to users with MATLAB, lies in the development of a transparent, adaptable, and reproducible CNN framework tailored for multiparameter water quality estimation, which extends beyond traditional empirical, site‑specific regression models by enabling non‑invasive, cost‑effective, and continuous monitoring from satellite platforms over a large, heterogeneous province‑scale domain. Copyright (c) 2026 Essam Sharaf El Din, Karim M. El Zahar, Ahmed Shaker All rights reserved Title: MATLAB codes for the Journal Paper "End-to-End Customized CNN Pipeline for Multiparameter Surface Water Quality Estimation from Sentinel-2 Imagery" CITATION: “The MATLAB codes used in this study are openly available at Zenodo: Essam Sharaf El Din, Karim M. El Zahar, Ahmed Shaker. (2026). MATLAB codes for ‘End-to-End Customized CNN Pipeline for Multiparameter Surface Water Quality Estimation from Sentinel-2 Imagery’. Zenodo. https://doi.org/10.5281/zenodo.18760111"