Urban　expansion　and　changes　in　land　use/land　cover　(LULC)　have　intensified　in　recent　decades　due　to　human　activity,　influencing　ecological　and　developmental　landscapes.　This　study　investigated　historical　and　projected　LULC　changes　and　urban　growth　patterns　in　the　districts　of　Multan　and　Sargodha,　Pakistan,　using　Landsat　satellite　imagery,　cloud　computing,　and　predictive　modelling　from　1990　to　2030.　The　analysis　of　satellite　images　was　grouped　into　four　time　periods　(1990-2000,　2000-2010,　2010-2020,　and　2020-2030).　The　Google　Earth　Engine　cloud-based　platform　facilitated　the　classification　of　Landsat　5　ETM　(1990,　2000,　and　2010)　and　Landsat　8　OLI　(2020)　images　using　the　Random　Forest　model.　A　simulation　model　integrating　Cellular　Automata　and　an　Artificial　Neural　Network　Multilayer　Perceptron　in　the　MOLUSCE　plugin　of　QGIS　was　employed　to　forecast　urban　growth　to　2030.　The　resulting　maps　showed　consistently　high　accuracy　levels　exceeding　92%　for　both　districts　across　all　time　periods.　The　analysis　revealed　that　Multan＇s　built-up　area　increased　from　240.56　km2　(6.58%)　in　1990　to　440.30　km2　(12.04%)　in　2020,　while　Sargodha　experienced　more　dramatic　growth　from　730.91　km2　(12.69%)　to　1,029.07　km2　(17.83%).　Vegetation　cover　remained　dominant　but　showed　significant　variations,　particularly　in　peri-urban　areas.　By　2030,　Multan＇s　urban　area　is　projected　to　stabilize　at　433.22　km2,　primarily　expanding　in　the　southeastern　direction.　Sargodha　is　expected　to　reach　1,404.97　km2,　showing　more　balanced　multi-directional　growth　toward　the　northeast　and　north.　The　study　presents　an　effective　analytical　method　integrating　cloud　processing,　GIS,　and　change　simulation　modeling　to　evaluate　urban　growth　spatiotemporal　patterns　and　LULC　changes.　This　approach　successfully　identified　the　main　LULC　transformations　and　trends　in　the　study　areas　while　highlighting　potential　urbanization　zones　where　opportunities　exist　for　developing　planned　and　managed　urban　settlements.