One of the most prevalent cancers among young men is testicular cancer, necessitating accurate risk assessment and early detection strategies. This paper presents a quantum networks-driven deep learning framework designed to predict testicular cancer risk factors, thereby enabling precision medicine interventions. Leveraging convolutional neural networks (CNNs), our approach analyzes multi-modal data, including genetic, demographic, and clinical information, to identify patterns indicative of the vulnerability to testicular cancer. Through extensive experimentation and validation on large-scale datasets, the model demonstrates superior performance in risk factor prediction compared to traditional methods. Moreover, the framework offers interpretability insights, facilitating a deeper understanding of the underlying biological mechanisms driving testicular cancer development. This research represents a significant advancement in the field of oncology, paving the way for personalized risk assessment and early intervention strategies tailored to individual patients