Drug resistance remains the leading cause of treatment failure in solid tumors. Conventional two-dimensional (2D) cultures and animal models cannot reproduce the dynamic tumor microenvironment (TME) cues driving resistance evolution—matrix stiffening, acidification, hypoxia, and enzymatic remodeling. Most 3D-bioprinted constructs remain static matrices and share this limitation. This review proposes the spatiotemporal dynamics of the TME as a design criterion for stimuli-responsive ("smart") bioinks, whose stiffness, degradability, drug-release kinetics, and geometry change in response to endogenous TME cues or external triggers. This review distills the TME into a quantitative design blueprint; examines the printability, cytocompatibility, and responsiveness requirements of smart inks, together with the dynamic chemistries enabling them; and summarizes strategies for reconstructing an evolving TME, using pancreatic ductal adenocarcinoma and glioblastoma as anchor case studies. The scope is restricted to cancer modeling and drug screening, excluding drug delivery and tissue regeneration. Current evidence remains proof-of-concept: printed models recapitulate resistance-associated phenotypes such as stiffness-mediated chemoresistance, cancer stem cell enrichment, dormancy, and phenotypic plasticity, whereas prospective validation of resistance models in patients has not been achieved. This review outlines the path from phenotype recapitulation to clinical prediction and analyzes prerequisites including vascularization, standardization, regulatory translation, and artificial intelligence (AI)-guided ink design.