The　center　of　human　settlements　is　in　the　cities,　which　must　have　high-quality　habitats　for　their　inhabitants.　Many　megachallenges　of　urbanization,　population　development,　global　advancement,　environmental　destruction,　traffic　management,　and　climate　change　must　be　addressed.　This　study　is　aimed　at　understanding　how　to　maintain　balanced　land　development　in　rapidly　urbanizing　towns　to　solve　this　challenge　and　mobility　issues.　Climate　and　weather　forecasts,　land　cover,　environmental　indices,　nonoptical　and　optical　wavelengths,　water　history,　and　air　quality　are　only　some　of　the　datasets　available　on　Google　Earth　Engine,　a　publicly　usable　data　repository.　Machine　learning　techniques,　i.e.,　random　forest　(RF),　support　vector　machine　(SVM),　and　classification　and　regression　tree　(CART),　are　used　to　monitor　spatial-temporal　change　regarding　water,　vegetation,　and　urbanization　for　Pakistan　from　2013　to　2021　using　Landsat　8.　The　detection　of　urban　land　suitability　concerning　multiple　metrics　such　as　ecological　response　variables,　environmental　tension,　socio-economic　development　potential,　and　natural　resource　potential　is　also　found.　Dataset　features　were　classified　as　bands　in　the　Google　Earth　Engine.　Moreover,　for　2020　and　2021,　classification　results　showing　the　change　in　water,　vegetation,　and　urbanization　are　also　represented　concerning　China　Pakistan　Economic　Corridor　(CPEC)　highway　and　the　railway　track　to　monitor　and　control　traffic　and　its　management.