Objective To construct the prediction model of prostate cancer (PCa) based on multimodal ultrasound parameters. Methods A total of 180 patients with suspected PCa inSecond Affiliated Hospital of Nantong University were enrolled as model group between January 2023 and May 2024. All patients underwent transrectal multimodal ultrasonography (two-dimensional ultrasound, color Doppler ultrasound, elastography, contrast-enhanced ultrasound) and puncture biopsy. According to pathological results, patients were divided into PCa group (n=93) and non-PCa group (n=87). The multimodal ultrasound parameters and general clinical data were recorded, and the significant factors were analyzed by multivariate Logisitc regression analysis. The prediction model of PCa was constructed, fit of the model was evaluated by Hosmer-Lemeshow test, and its predictive efficiency was detected by receiver operating characteristic (ROC) curves. A total of 32 patients with suspected PCa were enrolled as validation group for external verification between June and August 2024. Results The levels of serum prostate specific antigen (PSA) and prostate antibody density (PSAD) in PCa group were higher than those in non-PCa group (P<0.05). The positive rates of two-dimensional gray scale ultrasound and color Doppler ultrasound in PCa group were higher than those in non-PCa group, SWE parameters (Emax, Emean, Emin) and contrast-enhanced ultrasound parameters (peak intensity, intensity difference, absolute enhancement intensity per unit time) were also higher than those in non-PCa group (P<0.05). Multivariate Logisitc regression analysis showed that serum PSAD, Emax and intensity difference were independent predictors of PCa (P<0.05). The probability of PCa by prediction model was as follow: P=1/[1+e(-10.369+1.134 ×(serum PSAD)+ 1.359×(Emax)+1.089 ×(intensity difference)], Hosmer-Lemeshow χ2=3.696, P=0.883. ROC curves analysis showed that area under the curve (AUC) and 95%CI of the prediction model and external verification were (0.951, 0.929-0.974) and (0.932, 0.876-0.988), respectively (P<0.05). Conclusion Serum PSAD, Emax and intensity difference are independent predictors of PCa. The prediction model based on the above factors can effectively provide guidance for clinical diagnosis.