Introduction The treatment of locally advanced rectal cancer (LARC) is moving towards total neoadjuvant therapy and organ preservation. There are currently no clinical biomarkers which can accurately predict neoadjuvant therapy (NAT) response but body composition (BC) measures present as an emerging contender. The primary aim of the study was to determine if artificial intelligence (AI) derived body composition variables can predict pathological complete response (pCR) in patients with LARC. Methods LARC patients who underwent NAT followed by surgery from 2012-2023 were identified from the Australian Comprehensive Cancer Outcomes and Research Database registry (ACCORD). A validated in-house pre-trained 3D AI model was used to measure BC via CT images of the entire Lumbar-3 vertebral level to produce a volumetric measurement of visceral fat (VAT), subcutaneous fat (SAT) and skeletal muscle (SM). Multivariate analysis between patient body composition and histological outcomes was performed. Results Of 214 LARC patients treated with NAT, 22.4% of patients achieved pCR. SM volume (p=0.015) and age (p=0.03) were positively associated with pCR in both male and female patients. SAT volume was associated with decreased likelihood of pCR (p=0.059). Conclusion This is the first study in the literature utilising AI-measured 3D Body composition in LARC patients to assess their impact on pathological response. SM volume and age were positive predictors of pCR disease in both male and female patients following NAT for LARC. Future studies investigating the impact of body composition on clinical outcomes and patients on other neoadjuvant regimens such as TNT are potential avenues for further research.