Sun and sky glint pose significant challenges in remote sensing when evaluating water quality via satellite images in aquatic systems. These glint effects raise the water-leaving radiance, resulting in an overestimation of biogeochemical and optical parameters. Since conventional techniques, mainly designed for ocean and coastal waters, are less effective with high-spectral-resolution satellite data like Sentinel-2/MSI, it is crucial to enhance methods for masking and correcting these effects in satellite products. This study developed a glint-detection classifier using machine learning-based models and two new spectral indices with Sentinel-2 MSI imagery. The proposed spectral indices are called Normalized Difference Glint Index (NDGI) and Normalized Difference Glint Index 2 (NDGI2), and they are based on deep-blue (443 nm) and shortwave infrared (> 1600 nm) ranges. The machine learning model used a Random Forest Classifier (RF) and was trained on a total of 10,000 samples, achieving an accuracy of approximately 80% for glint detection. A comparison of four different glint correction algorithms was conducted and compared with in-situ measurements. The glint algorithms included SWIR-Subtraction (SubSWIR), Atmospheric Correction for OLI ’lite’ (ACOLITE), POLYnomial-based algorithm applied to MERIS (POLYMER), and Sun Glint Removal of Sentinel-2-like images (GRS). ACOLITE and GRS performed the best, reducing MAPE by about 59.86%. Among the glint algorithms, ACOLITE demonstrated a strong ability to preserve regions free of glint in the Sentinel-2/MSI imagery. The combined use of the mask and glint-correction algorithms can enhance time-series analysis, providing an effective solution for inland water monitoring using satellite images and supporting various applications.