Evidence before this study   We conducted an extensive literature search using Google Scholar without language restrictions, employing search terms such as “(Predicting OR Classifying OR Annotating) and (cancer hallmarks) AND (Deep OR Machine Learning) OR (Artificial Intelligence OR AI).” Despite notable advances in molecular oncology and computational methodologies, a critical gap remains: no existing machine learning or deep learning framework comprehensively predicts cancer hallmarks from tumor biopsy samples. Current research primarily targets specific molecular pathways associated with individual hallmarks, leaving clinicians without an integrated model to interpret hallmark activity at the level of an individual tumor. Moreover, the absence of wet-lab techniques capable of annotating all cancer hallmarks in biopsy samples has further impeded progress, limiting the clinical utility of hallmark-related insights for precision oncology.   Added value of this study   This study introduces OncoMark, a novel neural multi-task learning (N-MTL) framework designed to predict cancer hallmark activity from transcriptomic data obtained from biopsy samples. OncoMark addresses the lack of hallmark-specific data by generating synthetic biopsy datasets annotated with hallmark activity, meticulously modeled to reflect real-world tumor biology while maintaining clinical relevance. The framework employs a multi-task learning approach to capture interdependencies among hallmarks, advancing beyond isolated predictions to offer a holistic view of tumor biology. Validation on six independent datasets comprising 159 patient samples demonstrated its generalizability and reproducibility. Further external validation using eight datasets, encompassing over 11,679 cancer and 8348 normal patient samples, reinforced its robustness. To promote clinical integration, a user-friendly web-based tool was developed, enabling seamless access for oncologists and researchers.   Implications of all the available evidence   The OncoMark framework represents a transformative advancement in cancer diagnostics and treatment planning. By enabling accurate and reproducible prediction of hallmark activity from biopsy samples, this model paves the way for precision oncology at scale. Its ability to systematically capture hallmark interdependencies provides deeper insights into tumor behavior, guiding the development of individualized, targeted therapies. The incorporation of a web-based interface ensures the accessibility of this innovation to clinicians worldwide, bridging the gap between computational oncology and clinical practice. Following further validation and integration into healthcare workflows, OncoMark has the potential to improve cancer outcomes by delivering timely, cost-effective, and precise tumor analyses, facilitating informed therapeutic decision-making with unparalleled precision.