Oncological cytotoxic therapies, radiotherapy, and targeted or immunotherapy inevitably induce debilitating symptoms, such as fatigue, pain, and nausea or vomiting, severely impacting patients’ quality of life and treatment tolerance. Although traditional Chinese medicine (TCM) emphasizes personalized, holistic management through pattern differentiation, traditional practice is subjective and lacks standardization. This article proposes an artificial intelligence (AI)-secured four-diagnostic TCM tool for the management of oncological symptoms. The tool objectively quantifies TCM patterns in real time using digital tongue or face imaging, photoplethysmographic pulse waveforms, and pattern questionnaires, while concurrently assessing symptom severity using the MD Anderson Symptom Inventory (MDASI)-TCM. A pattern-symptom-technique smart matching algorithm then standardizes TCM intervention selection (e.g., acupoint patching, acupuncture), enabling a dynamic assessment-intervention-optimization closed-loop protocol that modernizes the TCM principle of “treating according to changing patterns.” This AI-driven approach shifts TCM from experience-based empiricism to objective data-driven practice, thereby enhancing the precision and standardization of integrative oncology by combining quantified patterns with MDASI-TCM symptom factors. The platform paves the way for the future integration of multi-omics data (imaging, genomics, proteomics, and metabolomics) to build predictive efficacy models and explore TCM patterns as prognostic biomarkers, ultimately providing a practical framework for improving the quality of life and delivering individualized integrative cancer care.