Asthma　is　a　diverse　disease　that　affects　over　300　million　individuals　globally.　The　prevalence　of　asthma　has　increased　by　50%　every　decade　since　the　1960s,　making　it　a　serious　global　health　issue.　In　addition　to　its　associated　high　mortality,　asthma　generates　large　economic　losses　due　to　the　degradation　of　patients’　quality　of　life　and　the　impairment　of　their　physical　fitness.　Asthma　research　has　evolved　in　recent　years　to　fully　analyze　why　certain　diseases　develop　based　on　a　variety　of　data　and　observations　of　patients’　performance.　The　advent　of　new　techniques　offers　good　opportunities　and　application　prospects　for　the　development　of　asthma　diagnosis　methods.　Over　the　last　few　decades,　techniques　like　data　mining　and　machine　learning　have　been　utilized　to　diagnose　asthma.　Nevertheless,　these　traditional　methods　are　unable　to　address　all　of　the　difficulties　associated　with　improving　a　small　dataset　to　increase　its　quantity,　quality,　and　feature　space　complexity　at　the　same　time.　In　this　study,　we　propose　a　sustainable　approach　to　asthma　diagnosis　using　advanced　machine　learning　techniques.　To　be　more　specific,　we　use　feature　selection　to　find　the　most　important　features,　data　augmentation　to　improve　the　dataset’s　resilience,　and　the　extreme　gradient　boosting　algorithm　for　classification.　Data　augmentation　in　the　proposed　method　involves　generating　synthetic　samples　to　increase　the　size　of　the　training　dataset,　which　is　then　utilized　to　enhance　the　training　data　initially.　This　could　lessen　the　phenomenon　of　imbalanced　data　related　to　asthma.　Then,　to　improve　diagnosis　accuracy　and　prioritize　significant　features,　the　extreme　gradient　boosting　technique　is　used.　The　outcomes　indicate　that　the　proposed　approach　performs　better　in　terms　of　diagnostic　accuracy　than　current　techniques.　Furthermore,　five　essential　features　are　extracted　to　help　physicians　diagnose　asthma.　©　2024　by　the　authors.