<p>Hormone-dependent cancers such as prostate, breast, and endometrial carcinomas rely on nuclear hormone receptors to sustain lineage identity and growth. Therapies targeting androgen, estrogen, or progesterone signaling are initially effective but ultimately impose selective pressures that drive resistance through lineage plasticity, the ability of tumor cells to abandon their native identity and adopt alternative cellular fates. While the biological consequences of lineage plasticity are increasingly recognized, a major challenge lies in defining the regulatory programs that govern these cell fate transitions and determine their stability, directionality, and therapeutic vulnerability. In this review, we focus on the regulatory modules that underlie lineage plasticity in hormone-driven cancers. We highlight how integrative multi-omics approaches spanning genomic, transcriptomic, epigenomic, proteomic, and chromatin-level layers have enabled the identification of transcriptional and epigenetic programs that destabilize lineage fidelity. We discuss how single-cell and spatial technologies have revealed intermediate states, rare subpopulations, and microenvironmental influences that shape plasticity trajectories. Finally, we emphasize the role of artificial intelligence and machine learning as integrative tools to reconstruct gene regulatory circuits, infer fate transitions, and connect molecular programs to phenotypic outcomes. By synthesizing these biological insights across experimental and computational modalities, we propose a conceptual framework for understanding lineage plasticity in hormone-driven cancers, with emphasis on prostate and breast malignancies. This integrated perspective highlights how regulatory programs governing cell identity can be revealed, perturbed, and potentially constrained, offering opportunities to identify biomarkers, expose therapeutic vulnerabilities, and ultimately translate mechanistic understanding into strategies that limit resistance.</p>