In　Pakistan,　agriculture　is　one　of　the　most　common　and　least　lucrative　professions.　It　provides　between　18%　and　25%　of　Pakistan＇s　overall　gross　domestic　product　(GDP).　The　majority　of　Pakistan＇s　crops,　like　cotton,　are　completely　weather-dependent.　Regarding　this,　farmers　are　constantly　attempting　to　implement　new　techniques　and　technology　to　boost　crop　yields.　Technology-based　approaches　to　crop　yield　analysis,　such　as　machine　learning　(ML)　and　data　mining,　are　causing　a　boom　in　the　agricultural　sector　by　altering　the　revenue　scenario　through　the　growth　of　the　best　crop.　By　utilizing　ML　algorithms　to　analyze　agriculture　climatic　data,　it　is　possible　to　increase　crop　yields.　The　proposed　research　was　carried　out　in　two　dimensions.　Initially,　field　observations　were　made　to　determine　the　effects　of　daily　variations　in　meteorological　parameters,　such　as　rainfall,　temperature,　and　wind,　on　plant　growth　and　development　at　each　phonological　stage　of　cotton　crop　production.　Throughout　the　Kharif　Seasons　2005-2020,　various　phonological　stages　of　the　cotton　crop　grown　in　the　fields　of　the　Ayyub　Agriculture　Research　Institute　in　Faisalabad　(Central　Punjab)　were　monitored　using　meteorological　and　phonological　observations,　as　well　as　soil　data.　Finally,　the　cotton　prediction　framework　as　Random　Forest　Extreme　Gradient　(RFXG)　has　been　proposed　to　predict　cotton　production　based　on　observed　data.　RFXG　concentrates　on　the　quantification　of　machine　learning　algorithms　and　their　practical　application.　The　workings　of　RFXG　have　been　divided　into　two　phases.　In　the　very　first　phase　of　data　collection,　preprocessing,　attribute　selection,　and　data　splitting　have　been　presented.　In　the　following　phase,　prediction　and　evaluation　were　developed.　The　comparative　results　show　that　the　prediction　results　of　the　proposed　RFXG　using　the　optimization　algorithm　are　significantly　improved　by　0.05　RMSE　(Root　Mean　Square　Error)　in　comparison　to　the　traditional　Extreme　Gradient　Boost　(XGB)　model,　which　has　a　RMSE　of　0.07.　Proposed　technique　also　compared　with　some　baseline　approaches　of　cotton　predication.　Comparison　shows　that　proposed　technique　achieves　better　results　as　compared　to　baseline　approaches.　The　proposed　RFXG　model　(ensemble-based　method)　can　bag,　stack,　and　boost,　making　it　fast　and　efficient　predications　as　compared　with　existing　approaches.　Bagging　averages,　the　results　of　numerous　decision　tree　fit　to　different　subsets　of　the　same　dataset　to　increase　accuracy.　The　proposed　study　will　be　very　useful　in　the　future　to　close　the　gap　between　the　current　yield　obtained　and　the　potential　yield　of　this　cultivar,　which　is　grown　in　Pakistan　and　other　cotton-growing　locations.