<p>Land use and land cover (LULC) dynamics are increasingly shaped by environmental and socio-economic activities, which has modified natural habitats and altered ecosystem functions across many African cities. However, comprehensive spatiotemporal assessments of land use and land cover (LULC) change in relation to population growth remain limited. Remote sensing combined with machine learning and deep learning algorithms provides an effective means of capturing spatiotemporal environmental changes associated with land use and land cover dynamics. This study examined land use and land cover (LULC) dynamics in Gaborone, Botswana, from 1991 to 2021 using the random forest classification algorithm. The study also predicted LULC changes for the year 2031 using One-dimensional convolutional neural networks (1D-CNN). Furthermore, statistical analysis (Pearson correlation and regression) was used to examine the relationship between population dynamics and LULC change. The Random Forest model produced more accurate land cover maps at ten-year intervals between 1991 and 2021. The LULC prediction for 2031 also produced acceptable values, with overall accuracy values of 0.88, 0.85, 0.82 and 0.84, respectively. The findings revealed a notable increase in the built-up area from 15% in 1991 to 31% in 2021, bare ground increased from 2% to 7%, while vegetation declined from 57% to 41% and agriculture decreased 5% to 3%. 1D-CNN projections for 2031 indicated that built-up areas will expand to 41%, with a decrease in vegetation and agricultural land. Statistical analysis revealed a positive correlation between population growth and the expansion of built-up areas and negatively correlated with the vegetation category. This study provides insights on future land use and land cover transformation, which can be used to achieve sustainable urban development.</p>