OBJECTIVE: To develop an artificial intelligence (AI) system for the early prediction of residual cancer burden (RCB) scores during neoadjuvant chemotherapy (NAC) in breast cancer. BACKGROUND: RCB III indicates drug resistance in breast cancer, and early detection methods are lacking. METHODS: This study enrolled 1048 patients with breast cancer from 4 institutions, who were all receiving NAC. Magnetic resonance images were collected at the pre-NAC and mid-NAC stages, and radiomics and deep learning features were extracted. A multitask AI system was developed to classify patients into 3 groups (RCB 0 to I, II, and III ) in the primary cohort (PC, n=335). Feature selection was conducted using the Mann-Whitney U test, Spearman analysis, least absolute shrinkage and selection operator regression, and the Boruta algorithm. Single-modality models were developed, followed by model integration. The AI system was validated in 3 external validation cohorts (EVCs, n=713). RESULTS: Among the patients, 442 (42.18%) were RCB 0 to I, 462 (44.08%) were RCB II, and 144 (13.74%) were RCB III. Model I achieved an area under the curve of 0.975 in the PC and 0.923 in the EVCs for differentiating RCB III from RCB 0 to II. Model II distinguished RCB 0 to I from RCB II-III, with an area under the curve of 0.976 in the PC and 0.910 in the EVCs. Subgroup analysis confirmed that the AI system was consistent across different clinical T stages and molecular subtypes. CONCLUSIONS: The multitask AI system offers a noninvasive tool for the early prediction of RCB scores in breast cancer, supporting clinical decision-making during NAC.