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<i>Background and Objectives:</i> Sigmoid resection still bears a considerable risk of complications. The primary aim was to evaluate and incorporate influencing factors of adverse perioperative outcomes following sigmoid resection into a nomogram-based prediction model. <i>Materials and Methods:</i> Patients from a prospectively maintained database (2004–2022) who underwent either elective or emergency sigmoidectomy for diverticular disease were enrolled. A multivariate logistic regression model was constructed to identify patient-specific, disease-related, or surgical factors and preoperative laboratory results that may predict postoperative outcome. <i>Results:</i> Overall morbidity and mortality rates were 41.3% and 3.55%, respectively, in 282 included patients. Logistic regression analysis revealed preoperative hemoglobin levels (<i>p</i> = 0.042), ASA classification (<i>p</i> = 0.040), type of surgical access (<i>p</i> = 0.014), and operative time (<i>p</i> = 0.049) as significant predictors of an eventful postoperative course and enabled the establishment of a dynamic nomogram. Postoperative length of hospital stay was influenced by low preoperative hemoglobin (<i>p</i> = 0.018), ASA class 4 (<i>p</i> = 0.002), immunosuppression (<i>p</i> = 0.010), emergency intervention (<i>p</i> = 0.024), and operative time (<i>p</i> = 0.010). <i>Conclusions:</i> A nomogram-based scoring tool will help stratify risk and reduce preventable complications.