Surgical site infections (SSIs), among the most frequent healthcare-associated infections, require surveillance, but traditional methods are labour-intensive. We developed machine learning (ML) and rule-based models for the semi-automated detection of deep and organ/space SSIs using data from a prospective cohort of 3931 surgical patients. We assessed sensitivity and workload reduction (proportion of patients not requiring manual review) at a 0.5 decision threshold, and computed area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC). The best-performing ML models (Naïve Bayes and dense neural network) achieved sensitivity up to 0.90, AUROC up to 0.968, AUPRC up to 0.248, and workload reduction over 90%. The rule-based model showed higher sensitivity (0.954) but lower AUROC, AUPRC, and workload reduction. Our findings suggest that semi-automated approaches can support efficient and accurate SSI surveillance while reducing manual workload. Further validation in other settings is warranted.