To explore the predictive value and diagnostic efficacy of clinical characteristics, hematological indicators and composite indicators for anastomotic leakage (AL) after laparoscopic anterior resection of the rectum in patients with colorectal cancer, and construct an early diagnosis model. Methods    The clinical data of 1195 rectal cancer patients who underwent laparoscopic anterior rectal resection at the Department of Gastric and Colorectal Surgery, General Surgery Center of the First Hospital of Jilin University between January 2019 and June 2024 were retrospective analyzed, with 839 cases in the training group and 356 cases in the validation group. Clinical characteristic indicators of patients and hematological parameters before and 1-3 days after surgery were collected. Patients were divided into the AL group and the non-AL group based on the occurrence of AL. 3 machine learning algorithms were employed to screen for differential characteristic indicators, and a multivariate Logistic regression was used to construct an early diagnosis model of AL, with the model effect verified in the validation group.  Results    A total of 83 of 1195 patients were diagnosed with AL, accounting for 7.0%. 3 machine learning algorithms identified 8 differential indicators (WBC, CAR on the second day after surgery and WBC, PNI, NLR, dNLR, WLR, CAR on the third day after surgery). The model constructed by multivariate Logistic regression was composed of WBC, WLR and CAR on the third day after surgery, with P values of 0.008, 0.004 and <0.0001, respectively, and OR values of 1.2 (95%CI 1.08-1.35), 1.05 (95%CI 1.01-1.08) and 1.61 (95%CI 1.39-1.87), respectively. In the training group, the area under the ROC curve of the model was 0.851 (95%CI 0.786-0.916), with a sensitivity of 75.9% and a specificity of 86.9%. In the validation group, the area under the ROC curve could also reach 0.808 (95%CI 0.719-0.900), with a sensitivity of 86.2% and a specificity of 67.3%. Conclusion    WBC, WLR and CAR on the third day after surgery are independent risk factors for AL after laparoscopic anterior resection of the rectum. The Logistic regression model constructed by these indicators can be used for early and accurate diagnosis of AL, providing a clinical basis for early intervention in AL patients.