BACKGROUND:　Moso　bamboo　(Phyllostachys　edulis)　plays　a　pivotal　role　in　the　global　carbon　cycle　because　of　its　rapid　growth　and　significant　ecological　benefits.　Accurate　estimation　of　its　aboveground　biomass　(AGB)　is　therefore　essential　for　effective　carbon　management.　However,　the　influence　of　its　primary　leaf-feeding　pest,　Pantana　phyllostachysae　Chao　(P.　phyllostachysae),　on　AGB　remains　poorly　understood,　potentially　compromising　estimation　accuracy.　This　study　aims　to　develop　allometric　equations　and　integrate　them　with　machine　learning　algorithms　to　accurately　estimate　the　AGB　of　Moso　bamboo　forests　under　varying　levels　of　pest　stress.　RESULTS:　Allometric　equations　exhibited　strong　estimation　performance　across　all　pest　infestation　levels,　with　R2　values　exceeding　0.93,　root　mean　square　error　(RMSE)　values　below　0.66　kg,　and　mean　absolute　error　(MAE) values　under　0.51　kg.　Among　the　machine　learning　approaches　evaluated,　the　Extreme　Gradient　Boosting　(XGBoost)　algorithm　demonstrated　superior　performance,　yielding　an　R2　of　0.8593,　RMSE　of　0.5176　kg,　and　MAE　of　0.4313　kg.　A　clear　negative　correlation　was　identified　between　the　severity　of　P.　phyllostachysae　infestation　and　AGB,　with　biomass　values　decreasing　progressively　from　healthy　to　severely　infested　stands.　CONCLUSION:　Incorporating　pest　factors　into　AGB　estimation　models　significantly　enhances　model　accuracy　and　captures　the　nuanced　effects　of　pest　stress　on　biomass　accumulation.　This　integration　improves　model　generalizability　and　ecological　relevance,　offering　valuable　insights　for　sustainable　forest　management　and　carbon　accounting.　The　findings　highlight　the　importance　of　explicitly　considering　pest　dynamics　in　biomass　modeling　and　carbon　management　strategies,　laying　a　robust　foundation　for　future　research　on　pest–biomass　interactions　in　forest　ecosystems.　©　2025　Society　of　Chemical　Industry.　©　2025　Society　of　Chemical　Industry.