As　industrial　development　drives　the　increasing　demand　for　steel,　accurate　estimation　of　the　material＇s　fatigue　strength　has　become　crucial.　Fatigue　strength,　a　critical　mechanical　property　of　steel,　is　a　primary　factor　in　component　failure　within　engineering　applications.　Traditional　fatigue　testing　is　both　costly　and　time-consuming,　and　fatigue　failure　can　lead　to　severe　consequences.　Therefore,　the　need　to　develop　faster　and　more　efficient　methods　for　predicting　fatigue　strength　is　evident.　In　this　paper,　a　fatigue　strength　dataset　was　established,　incorporating　data　on　material　element　composition,　physical　properties,　and　mechanical　performance　parameters　that　influence　fatigue　strength.　A　machine　learning　regression　model　was　then　applied　to　facilitate　rapid　and　efficient　fatigue　strength　prediction　of　ferrous　alloys.　Twenty　characteristic　parameters,　selected　for　their　practical　relevance　in　engineering　applications,　were　used　as　input　variables,　with　fatigue　strength　as　the　output.　Multiple　algorithms　were　trained　on　the　dataset,　and　a　deep　learning　regression　model　was　employed　for　the　prediction　of　fatigue　strength.　The　performance　of　the　models　was　evaluated　using　metrics　such　as　MAE,　RMSE,　R2,　and　MAPE.　The　results　demonstrated　the　superiority　of　the　proposed　models　and　the　effectiveness　of　the　applied　methodologies.