Objective: To establish and validate a predictive framework for microvascular invasion (MVI) in hepatocellular carcinoma (HCC) cases characterized by tumors ≤3 cm in diameter and limited to three lesions. This study leverages both contrast-enhanced ultrasound (CEUS) findings and clinical data for prediction. Methods: This study enrolled HCC patients undergoing hepatectomy at our center between January 2017 and January 2023. Histopathological examination defined the presence of MVI, segregating subjects into MVI-positive and MVI-negative cohorts. Participants were randomly divided into training (70%) and validation (30%) sets. Initial analysis utilized chi-square, t-tests, or Mann-Whitney U tests for identifying variable disparities between MVI cohorts within the training set. Subsequent multivariable logistic regression discerned independent MVI predictors, facilitating the construction of clinical, ultrasound, and integrative predictive models. These models' performance was appraised through Area Under the Receiver Operating Characteristic Curve (AUC), calibration analysis, and decision curve evaluation. Results: The study incorporated 423 individuals, with 171 diagnosed with MVI and 252 without. Analysis within the 296-patient training cohort highlighted significant variations in LI-RADS classification, alpha-fetoprotein (AFP) levels, changes in tumor size post-contrast, and gender across MVI statuses. Multivariate regression underscored elevated LI-RADS classification, increased AFP, and tumor size enlargement post-contrast as independent MVI risk contributors, yielding odds ratios of 1.675, 1.585, and 1.292, respectively. Comparative evaluation of the models revealed AUCs of 0.624 (clinical), 0.669 (ultrasound), and 0.851 (integrated), signifying the integrated model's superior predictive accuracy and clinical utility. Conclusion: Our integrative model, combining CEUS and clinical metrics, outperforms single-factor models in predicting MVI in small HCC lesions. This model promises enhanced guidance for tailoring individualized therapeutic strategies.