Emerging　applications　are　placing　increasing　demands　on　wireless　networks,　particularly　in　terms　of　ensuring　reliable　communication　for　control-related　information.　However,　the　complexity　of　network　architectures　and　the　growing　number　of　user　devices　present　significant　challenges　in　achieving　reliable　multiple　access.　In　this　paper,　we　present　a　framework　that　utilizes　machine　learning　(ML)　to　meet　the　need　for　reliable　access　in　unmanned　aerial　vehicle　(UAV)　networks.　The　K-means　algorithm　is　employed　to　cluster　users　according　to　their　communication　reliability　requirements,　grouping　together　users　with　similar　demands　within　each　cluster.　Each　cluster　adopts　a　different　access　strategy:　clusters　with　lower　reliability　requirements　utilize　non-orthogonal　multiple　access　to　enhance　spectrum　efficiency,　while　clusters　with　higher　reliability　requirements　employ　orthogonal　multiple　access　to　ensure　reliability.　Taking　into　account　the　impact　of　UAV　altitude　and　power　allocation　schemes　on　reliability,　we　propose　an　iterative　algorithm　to　optimize　the　UAV　altitude　and　power　allocation　factors,　aiming　to　maximize　UAV　coverage　while　meeting　the　users＇　reliability　requirements.　The　simulation　results　validate　the　effectiveness　of　the　proposed　ML-based　reliable　access　scheme,　highlighting　its　potential　to　enhance　the　design　and　deployment　of　reliable　communication　in　future　UAV　networks.