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Growing global concern over natural resource degradation due to urbanisation and population growth emphasizes the critical need for innovative solutions. Addressing this imperative, our study pioneers the integration of cutting-edge artificial intelligence (AI) methods to investigate crucial changes in vegetation density. In this context, a hybrid model, which harmoniously integrates conventional artificial neural network (ANN) models with the innovative Wavelet-ANN (W-ANN) approach, was employed in two case pilot areas, namely on Alanya in Antalya and Iznik in Bursa, Turkiye, renowned for their distinct ecosystems and land cover patterns. By employing diverse data sources, encompassing satellite-derived metrics such as the Enhanced Vegetation Index (EVI) and Land Surface Temperature (LST) from the MODIS/Terra satellite, alongside atmospheric data, our investigation intricately models temporal vegetation dynamics extending to the year 2030. Remarkably, the W-ANN model demonstrates better predictive performance compared to conventional methodologies. It anticipates a substantial 21.4% reduction in vegetation biomass density for Iznik, achieving a minimal 5.4% error probability. Similarly, for Alanya, the model forecasts a notable 6.6% decrease with a remarkably low 2% error probability, both projections extending to the year 2030. Our study reveals a significant reduction in vegetation biomass density by comparing the projected values of the W-ANN model for 2030 with the observed data from 2018. These findings gain further support from an analysis of the Normalised Difference Built-up Index (NDBI) derived from Landsat satellites, affirming the exceptional efficacy of our innovative AI-driven approach in advancing the understanding of urbanisation's impact on ecosystems. • The hybrid approach significantly improved learning efficacy. • The findings indicate a substantial projected decrease in vegetation biomass density by 21.4% in Iznik from 2018 to 2030, with a remarkably low error probability of 5.4%. • In the region of Alanya, it is anticipated that the vegetation biomass density will experience a decline of 6.6% by 2030 in comparison to the measurements obtained in 2018, with a low error probability of 2%. • Quantify the correlation between urbanisation and vegetation density [ABSTRACT FROM AUTHOR]