Aero-engines　are　highly　complex　machines,　and　their　reliability　and　lifespan　directly　impact　flight　safety.　As　a　result,　aero-engine　reliability　has　become　a　significant　research　field.　This　paper　aims　to　comprehensively　summarize　the　progress　in　aero-engine　reliability　research.　First,　we　analyzed　the　existing　structures　of　aircraft　engines,　as　well　as　the　current　research　on　reliability　and　commonly　used　estimation　methods.　Second,　we　reviewed　the　techniques　employed　for　monitoring　operational　status　and　diagnosing　faults　in　aircraft　engines.　Third,　we　summarized　the　optimization　methods　frequently　used　in　fault　diagnosis　and　lifespan　prediction.　Fourth,　we　conducted　a　bibliometric　analysis.　Finally,　we　identified　potential　challenges　for　future　research.　Based　on　our　literature　review,　we　found　that:　1)　mathematical　modeling-based　and　data-driven　approaches　are　currently　the　most　commonly　used　methods　for　performance　monitoring;　2)　neural　networks　and　deep　learning　are　the　most　popular　methods　for　fault　diagnosis　and　lifespan　prediction;　3)　the　bibliometric　analysis　reveals　that　China＇s　interest　in　aero-engine　reliability　research　techniques　is　higher　than　that　of　other　countries;　and　4)　further　development　is　needed　in　the　areas　of　overall　system　reliability,　real-world　usage　scenario　studies,　and　advanced　simulation　and　modeling.