Ghana's efforts to incorporate evolutionary computation and bio-mathematical models into healthcare are critical and
revolutionary in a time when non-personalized treatments were linked to over 10 million deaths worldwide in 2023. Given
Ghana's low baseline of only 7% computational healthcare adoption by 2024, this study critically examined how computational
advances such as genetic algorithms, differential evolution methods, and swarm intelligence can improve treatment
personalization between 2020 and 2024. Statistical studies, such as regression and correlation, were carried out using a
quantitative, descriptive, and explanatory design that was exclusively based on secondary data from 138 health facilities located
throughout Greater Accra, Ashanti, and Eastern Regions. The main results demonstrated a 17% increase in treatment efficacy, a
38% decrease in adverse drug reactions, a 27.8% reduction in recovery times, and a significant improvement in patient
satisfaction. The overall model fit was strong, with an Adjusted R2 of 0.752 and a correlation coefficient of 0.845 for genetic
algorithms. The study found that while evolutionary computation significantly improved patient-centered outcomes, there are still
gaps in knowledge and infrastructure, particularly in rural clinics. The ramifications highlight the necessity of national initiatives
that prioritize developing hybrid computational techniques, improving healthcare professionals' skills, and growing bioinformatics
infrastructure. To expedite Ghana's shift to fully personalized medicine, policymakers should prioritize scaling computational
innovations nationally, healthcare managers should invest in workforce capacity building, researchers should investigate hybrid
evolutionary models, and international partnerships should be encouraged.