Measurement　of　gas　diffusion　coefficient　(Dp)　of　biochar-amended　soil　(BAS)　under　varying　conditions　is　essential　for　assessing　the　adsorption　capacity　and　water/gas　diffusion　in　compacted　BAS.　However,　there　is　no　established　equation　of　Dp　available　on　this　topic.　Also,　the　factors　influencing　gas　diffusion　in　BAS　have　not　been　properly　studied　and　remain　unclear.　Various　machine　learning　models　were　employed　in　this　paper　to　learn　and　predict　the　Dp　of　BAS　based　on　experimental　data.　Six　factors　(i.e.,　degree　of　compaction　(DOC),　biochar　content　(BC),　soil　air　content　(SAC),　gravimetric　water　content　(GWC),　degree　of　saturation　(DS),　and　porosity)　are　considered　for　testing　the　prediction　models.　The　epsilon　radial　basis　function　support　vector　regression　model　showed　better　accuracy　and　predictive　performance　(R=0.9925)　than　other　models　and　was　further　improved　by　applying　the　feature　selection　technique　using　the　multiple　linear　regression　and　tree-based　models　(R=0.9937).　The　results　reveal　that　SAC,　DS,　and　porosity　are　the　main　predictor　variables.　The　SAC　proved　to　be　the　most　influential　predictor　variable　based　on　the　estimated　p-value.　Furthermore,　the　optimal　Dp　was　established　for　the　various　DOC　and　BC,　which　could　be　useful　in　designing　engineered　landfill　covers.　The　accurate　model　prediction　and　relative　importance　of　the　predictor　variables　could　significantly　minimize　the　experimental　work　volume　required　to　determine　Dp,　thereby　saving　time　and　cost.　©　2022　Elsevier　B.V.