<p>Robotic surgery, with its advanced mechanical arm and enhanced visual capabilities, has been regarded as a development of laparoscopic surgery. The two surgical methods complement each other and seem to achieve similar oncological results. This study, based on a large sample, develops and validates a predictive model for anastomotic complications followed by propensity-matched analysis to compare the oncological outcomes and perioperative anastomotic complications of laparoscopic and robotic surgery. All patients who underwent radical resection have been collected prospectively. After censoring for inclusion criteria, we performed a propensity-matching analysis. We conducted a thorough compilation of postoperative examination data, and Kaplan–Meier (K-M) survival curves were generated to assess the oncological outcomes while the proportion of anastomotic complications was evaluated accurately. Meanwhile, the final model was built using logistic regression with a 7:3 ratio. The training dataset was subjected to both univariate and multivariate analyses to refine the predictive model. Receiver operating characteristic (ROC), calibration plots, and Decision curve analysis (DCA) were used to measure the model’s clinical utility. Eventually, patients matched 1:1 by propensity score were divided into the robotic and laparoscopic groups, with 210 cases included in each group. Although the operative time was longer in robotic surgery (<i>P</i> = 0.011), the earlier time to first flatus passage without ileostomy (<i>P</i> = 0.040) and the earlier time to liquid diet without ileostomy (<i>P</i> = 0.030) were shorter with statistical difference. The oncological outcomes and occurrence of other complications were comparable between the two approaches. The model incorporated 7 factors including: BMI (OR = 0.27), neoadjuvant chemoradiotherapy (nCRT, OR = 1.70), distance from anal verge (OR = 2.01), gender (OR = 0.65), size of tumor (OR = 1.71), diabetes (OR = 0.22), and operative time (OR = 0.19). The model exhibited strong discriminative ability in ROC with area under curve (AUC) values of 0.858 in the training set and 0.823 in the validation set. DCA and calibration plots revealed that the model possesses substantial clinical utility. For mid-low rectal cancer, robotic surgery emerges as a viable method, offering comparable short-term oncological results and better performance of perioperative anastomotic complications relative to laparoscopic surgery. Meanwhile, the model serves as a valuable tool for clinicians, enabling them to identify patients who are at risk for developing anastomotic complications.</p>