Age-related　cognitive　decline　is　a　global　phenomenon　that　affects　individuals　worldwide.　The　course　and　extent　of　this　decline　are　influenced　by　numerous　factors,　such　as　genetics,　lifestyle,　education,　and　cognitive　engagement.　The　theory　of　brain　and　cognitive　reserve/maintenance　posits　that　these　factors　have　a　significant　impact　on　the　degree　of　cognitive　decline　and　overall　brain　health.　However,　the　absence　of　standardized　definitions　and　measurements　for　these　terms　creates　ambiguity　in　research.　To　address　this　issue,　we　utilized　a　robust　and　systematic　experimental　paradigm,　employing　a　considerably　large　subject　pool　comprising　17,030　participants　from　the　UK　Biobank.　Utilizing　advanced　machine　learning　methodologies,　we　were　able　to　accurately　quantify　both　brain　maintenance　(BM)　and　cognitive　maintenance　(CM),　making　use　of　six　distinct　MRI　modalities　and　nine　distinct　cognitive　capabilities.　Our　study　successfully　identified　several　significant　features　that　were　meaningfully　associated　with　both　BM　and　CM　outcomes.　The　results　of　our　study　demonstrate　that　lifestyle　factors　play　a　significant　role　in　influencing　both　BM　and　CM　through　unique　and　independent　mechanisms.　Specifically,　our　study　found　that　health　status　is　a　critical　determinant　of　BM,　while　diabetes　was　found　to　be　moderately　associated　with　CM.　Furthermore,　our　study　revealed　a　positive　correlation　between　BM/CM　and　cognitive　reserve.　By　carefully　considering　the　unique　and　independent　mechanisms　that　govern　both　BM　and　CM,　as　well　as　their　correlation　with　cognitive　reserve,　our　study　has　provided　valuable　insight　into　the　various　strategies　that　may　be　leveraged　to　promote　sustainable　interventions　to　enhance　cognitive　and　brain　health　across　the　lifespan.