Flexural　size　effect,　originating　from　the　fracture　characteristics　of　materials,　is　a　common　phenomenon　in　concrete.　Conventionally,　time-consuming　and　labor-intensive　experiments　are　required　to　investigate　the　flexural　size　effect　and　fracture　behaviors　of　concrete.　To　tackle　the　limitations,　a　data-driven　approach　was　adopted　to　predict　the　multifactor-influenced　flexural　size　effect　and　fracture　behaviors　of　concrete　by　gene　expression　programming　(GEP)　due　to　its　capability　of　addressing　non-linear　problems　and　developing　empirical　equations　with　multiple　input　variables.　Results　show　that　the　GEP　models　can　accurately　predict　nominal　flexural　strength　(R2,　0.890)　and　fracture　toughness　(R2,　0.946).　Parametric　analysis　reveals　that　the　compressive　strength　and　tensile　strain　capacity　positively　impact　the　nominal　flexural　strength　and　fracture　toughness　of　concrete.　Based　on　the　GEP　model,　a　multifactor-influenced　size　effect　law　(SEL)　is　proposed　to　predict　the　nominal　flexural　strength　by　incorporating　both　material　and　geometric　parameters,　removing　the　need　for　extensive　experimental　investigations.　The　findings　provide　generalized　models　to　predict　the　nominal　flexural　strength　and　fracture　toughness　of　various　materials　at　different　sizes.