Poverty　continues　to　pose　significant　global　challenges.　Analyzing　poverty　distribution　is　pivotal　for　identifying　spatial　and　demographic　disparities,　informing　targeted　policy　interventions,　and　fostering　inclusive　and　equitable　development.　The　absence　of　a　worldwide　pixel-scale　time-series　poverty　dataset　has　hampered　effective　policy　formulation.　To　address　this　gap,　we　employ　the　international　wealth　index　(IWI)　derived　from　household　survey　data　to　represent　poverty　levels.　Subsequently,　a　random　forest　regression　model　was　constructed,　with　IWI　serving　as　the　dependent　variable　and　representative　features　extracted　from　nighttime　lights,　land　cover,　digital　elevation　model,　and　World　Bank　statistical　data　serving　as　independent　variables.　This　yielded　a　global　map　of　the　IWI　for　low-　and　middle-income　nations　at　a　10-km　resolution　spanning　2005　to　2020.　The　model　demonstrated　robust　performance　with　an　R2　value　of　0.74.　Over　the　studied　period,　areas　and　populations　with　IWI　＜=　50　decreased　by　8.85%　and　16.17%,　indicating　a　steady　decrease　in　global　poverty　regions.　Changes　in　the　IWI　at　the　pixel　scale　indicate　that　areas　closer　to　cities　have　faster　growth　rates.　Furthermore,　our　poverty　estimation　models　present　a　novel　method　for　real-time　pixel-scale　poverty　assessments.　This　study　provides　valuable　insights　into　the　dynamics　of　poverty,　both　globally　and　nationally.