Coastal waters, crucial for ecology, are threatened by pollution and eutrophication caused by human activities. Monitoring water quality, particularly parameters such as Chlorophyll-a (Chl-a) and Total Suspended Matter (TSM), is essential for sustaining biodiversity and managing aquatic ecosystems. Although in-situ measurement methods are considered reliable, they are expensive, labour-intensive, and spatially limited, which poses challenges for large-scale monitoring. Satellite remote sensing offers an alternative, providing large-scale data for assessing water quality over time. This study utilizes Sentinel-2 Level-1C (Top of Atmosphere reflectance) satellite imagery and the Google Earth Engine (GEE) platform to monitor temporal changes in Izmir and Erdek bays from 2018 to 2024. Indices such as the Normalized Difference Chlorophyll Index (NDCI) and the Normalized Difference Aquatic Vegetation Index (NDAVI) were used for a time series analysis to evaluate water quality, showing high values in both study areas during 2020, particularly in summer, when the correlation was strongest. The Coast 2 Regional CoastColor (C2RCC) algorithm was applied to retrieve Chl-a and TSM values. According to the time series analysis results, Chl-a and TSM parameters were calculated as 7.87 mg/m³ and 2.51 g/m³ and 10.18 mg/m³ and 1.15 g/m³ respectively for Izmir and Erdek bays. Results also show a correlation between the indices and water quality parameters. This suggests that satellite-based methods effectively monitor complex aquatic ecosystems without in-situ measurements. In order to increase accuracy and reliability, future work involves integrating advanced modelling techniques, such as deep learning networks, with remote sensing data into the GEE cloud-based platform.