Hepatocellular carcinoma (HCC) is the most common primary liver malignancy, presenting a wide spectrum of prognoses and treatment options, including liver transplantation, resection, local-regional therapies, chemotherapy, and immunotherapy. Among these, transarterial chemoembolization (TACE) remains the standard of care for patients with unresectable HCC without extrahepatic spread and with preserved venous flow. Artificial intelligence (AI) is increasingly utilized in HCC research for detection, segmentation, and prognostication, yet the development of robust AI models requires diverse and comprehensive datasets integrating imaging and clinical variables. However, publicly available datasets remain limited, often lacking tumor segmentations and clinical outcome data necessary for AI model validation. To address this gap, we introduce the WAW-TACE dataset, an annotated dataset encompassing baseline clinical data, pre-TACE multiphase CT imaging with segmentations, radiologic response assessments, and survival outcomes, offering a valuable resource for advancing AI-driven HCC research.