Evaluate how TKIs affect the population dynamics of leukemic cells during treatment according an populational decaying model, with the goal of optimizing treatment strategies for CML. Using ELN guidelines, this study modeled BCR::ABL1 p210 decay over 12 months post-TKI treatment via logarithmic interpolation, yielding a predictive equation. Key assumptions included a closed population, non-overlapping generations, density-independent mortality, discrete time, and exponential decay. Wolfram Mathematica was used for curve fitting. This study uses ELN guidelines and logarithmic interpolation to model BCR::ABL1 decay in CML patients undergoing TKI therapy. Starting from month 3, the equation (y = 99.72e−0.766x), derived from RT-qPCR data, predicts BCR::ABL1 levels with an R2 of 0.99, indicating high accuracy. The model, reflecting the clinical timeline of BCR::ABL1 monitoring, demonstrates the efficacy of TKI therapy and offers a reliable framework for predicting BCR::ABL1 trajectories, aiding in treatment monitoring and decision-making. This study uses evolutionary and ecological modeling to evaluate BCR::ABL1 decline in CML patients undergoing TKI therapy. Initial BCR::ABL1 levels and decay rates are critical for predicting treatment success according to decaying model. This approach suggests more aggressive initial treatment for high BCR::ABL1 levels, using potent TKIs and combination therapies. This approach aims to overcome resistance, enhance treatment efficacy, and personalize therapy based on patient-specific characteristics. This research uses evolutionary, and population decline models to study CML treatment. Initial BCR::ABL1 levels could predict treatment response, suggesting personalized strategies could improve outcomes. Further validation is needed.