A deeper understanding of the influence of the El Nino-Southern Oscillation (ENSO) on climate variability in a region is essential to anticipate its future impacts.Vegetation dynamics can be an indicator of climate variability due to its sensitivity to environmental changes. Therefore, this study aims to analyse vegetation dynamics based on Normalised Difference Vegetation Index (NDVI) to identify differences in climate variability in the La Nina and El Nino phases in Gowa Regency, and to analyse the relationship between climate parameters and vegetation dynamics. The observed La Nina phase in 2022 and El Nino in 2023 were confirmed based on the Multivariate ENSO Index (MEI). Remote sensing data from Sentinel-2 L2A satellite images were analysed using ArcGIS 10.8 software with the NDVI method. Furthermore, spatial and temporal analyses were conducted to compare vegetation dynamics in both phases. The results showed a significant difference in vegetation dynamics between La Nina and El Nino phases. During the La Nina phase in 2022, NDVI showed stability and dominance of areas with high vegetation indices. Climate variability in this phase is characterised by stable rainfall and air temperature, which support optimal vegetation growth. In contrast, during the El Nino phase in 2023, NDVI shows a significant decrease in areas with a high vegetation index and a significant increase in areas without vegetation. Climate variability during this phase is characterised by very low rainfall and higher air temperatures, which has implications for reduced productivity and vegetation degradation. In addition, regression analyses showed that air temperature tends to have a greater influence than rainfall on vegetation dynamics. The regression model has an R² = 0.92 and Adjusted R² = 0.87, indicating strong predictive ability. Partially, the two variables showed no significant influence at the 95% confidence level. However, based on F statistics (F = 17.63; p = 0.02), both variables simultaneously had a significant influence on vegetation. This research can be used as a basis for formulating mitigation and adaptation strategies to climate variability in the future.