Shear　walls　are　typically　the　major　lateral　load-carrying　components　in　high-rise　buildings　owing　to　their　high　lateral　strength　and　stiffness.　This　study　introduces　a　technique　for　predicting　the　seismic　performance　of　reinforced　concrete　(RC)　walls　using　machine　learning　(ML)　methods.　Based　on　various　ML　algorithms,　predictive　models　were　developed　using　a　database　containing　experimental　data　of　429　RC　walls　collected　from　the　literature.　The　performance　of　each　pre-dictive　model　was　discussed　in　detail.　The　results　indicated　that　the　XGBoost　and　GB　algorithms　accurately　predicted　the　failure　modes　of　RC　walls　with　an　accuracy　of　97%.　The　gradient　boosting　and　random　forest　algorithms　performed　best　in　predicting　the　lateral　strength　and　ul-timate　drift　ratio　of　RC　walls,　with　a　mean　predicted-to-tested　strength　ratio　of　1.01　and　a　predicted-to-tested　ultimate　drift　ratio　of　1.08.　The　flexure-to-shear　strength　ratio　and　shear-to-span　ratio　of　RC　walls　had　a　greater　influence　on　the　failure　modes　of　RC　walls,　with　a　relative　importance　factor　of　54.1%　for　these　two　characteristics.　Boundary　longitudinal　reinforcement,　shear-to-span　ratio,　and　distributed　reinforcement　had　an　obvious　influence　on　the　lateral　strength　of　RC　walls　with　summation　of　relative　important　factors　of　over　80%.　The　greatest　influence　on　the　ultimate　drift　ratio　of　RC　walls　is　shear-to-span　ratio,　with　a　relative　importance　factor　of　34.1%.　The　comparisons　indicate　that　the　model　developed　in　this　study　can　predict　the　shear　strength　and　flexural　strength　of　RC　walls　more　accurate　and　efficient　than　the　existing　design　formulas.　Furthermore,　a　brief　boundary　that　could　separate　flexure-shear　failure　from　other　failure　modes　is　provided　based　on　the　ML　models.　Finally,　a　graphical　user　interface　(GUI)　platform　is　created　to　facilitate　the　practical　design　of　RC　walls.