Triple-negative breast cancer (TNBC) remains a major oncological challenge because of profound inter- and intra-tumoral heterogeneity, adaptive signalling, treatment resistance, and early recurrence despite frequent initial chemosensitivity. Conventional target-discovery approaches based on static single-protein associations and demographically restricted datasets may fail to capture this complexity and overlook population-associated molecular patterns. Network pharmacology, multi-omics integration, supervised machine learning, and graph neural networks offer opportunities to move from broad target lists toward context-aware, evidence-weighted prioritization. However, these approaches can generate “false precision” when biased datasets, batch effects, data leakage, weak external validation, black-box predictions, or overinterpretation of docking and molecular dynamics simulations are treated as biological proof. This review critically examines these limitations and proposes a qualitative evidence hierarchy that distinguishes hypothesis-generating computational associations from mechanistically supported, translationally strengthened, and cautionary evidence. Selected natural-product case studies illustrate how evidentiary strength depends on target engagement, mechanistic validation, pharmacological feasibility, and model relevance. We further propose a responsible AI-guided framework integrating population-aware multi-omics filtering, network and AI/ML prioritization, interpretability, structural and ADMET assessment, and progressive experimental validation. This framework aims to reduce false precision and improve the translational credibility of AI-enabled target discovery in TNBC.