A central challenge in evolutionary therapy is predicting tumor adaptation, yet a detailed quantitative understanding of the evolutionary dynamics of aneuploidy remains elusive. Here we introduce ALFA-K (Adaptive Local Fitness landscapes for Aneuploid Karyotypes), a novel method that infers chromosome-level karyotype fitness landscapes from longitudinal single-cell data. ALFA-K estimates the fitness of thousands of karyotypes closely related to observed populations, enabling robust prediction of emergent karyotypes not yet experimentally detected. We validated ALFA-K’s performance using synthetic data from an agent-based model and empirical data from cell lines passaged under diverse selective pressures, including cisplatin treatment. Analysis of these landscapes reveals several principles of cancer evolution with direct implications for designing and testing evolutionary therapies: 1) Therapeutic pressures and environmental context fundamentally reshape the fitness landscape by significantly modulating the selective value of copy number alterations. 2) Chromosome mis-segregation rates can dictate clonal dominance, suggesting that therapeutically modulating chromosomal instability could be used to steer tumor evolution. 3) The fitness consequences of CNAs are highly contingent on the parental karyotype, highlighting the critical role of genomic context in predicting evolutionary trajectories. 4) Whole-genome doubling facilitates more rapid karyotypic diversification by buffering cells against deleterious CNAs, thereby opening new adaptive pathways. Ultimately, ALFA-K inferred landscapes provide a quantitative basis to forecast tumor progression and rationally design strategies to pre-empt or control therapeutic resistance.