The study offers an in-depth investigation of the impact of climate conditions on various terrain features in the Kaffiøyra area, southwest Svalbard, focusing on the changes of shorelines, glacier termini, and outwash areas. We utilized cost-effective, consistent remote sensing data and GIS techniques to analyze historical long-term changes in these terrain features, evaluated the effects of temperature variations on glacier retreat, and developed a predictive model for future glacier area changes based on temperature data from climate change scenarios and the ground observation station near the site. The results demonstrated a range of responses in shoreline stability and changes across different zones in the study site. Based on the predefined baseline, the glacier-shorelines (tidewater glacier) exhibit significant erosion, with the averaged rates varying from -64.7 to + 9.2 m/yr. However, the land-shorelines show relatively stable, with changes ranging from -2.2 to +3.7 m/yr, and the outwash areas exhibited minor increases of less than 10% as compared to the data obtained in 1985. The analysis of seven glacier termini indicated a general reduction in the glacier area; notably, Aavatsmarkbreen, a tidewater glacier, retained only 32% of its original size by 2023. The land glaciers such as Waldemarbreen, Irenebreen, Elisebreen, Eivindbreen, Andreasbreen, and Oliverbreen, preserve 54.8%, 61.3%, 65.0%, 74.0%, 55.5%, and 43.7% of their areas, respectively. A marked negative linear correlation between temperature and remaining glacier areas was observed, enabling the prediction of future glacier variations under various climate conditions. The study outlines the evolution of terrain features in Kaffiøyra in nearly 40 years. Results show the relative stability of land-shorelines, significant glacier retreats in response to warming temperatures, and slight growth in outwash areas. The study highlights the critical need for robust land monitoring systems that enhance our understanding and enable effective decision-making through predictive modeling of climate impacts.