Media budget allocation remains a major challenge in the advertising industry. Inefficient spending and biased budgeting decisions often cause underperforming campaigns. It is a challenge for advertisers to strike the right balance between traditional media like TV, radio, press and the digital media. This not only wastes resources but reduces campaign impact. Media mix problem revolves around media performance and return on investment. This study develops a data-driven media mix determination model using machine learning and genetic algorithms. The goal is to maximize audience reach while minimizing costs. The model focuses on key quantitative factors such as cost-effectiveness, media efficiency, and saturation points. The use of data collected from the Sri Lankan market ensures the practical relevance of the study. Supervised machine learning models such as decision trees, random forests, XGBoost, and LightGBM are tested to understand the complex, non-linear behavior of media performance. The above models predict how different channels respond to increased spending. Curve smoothing enables the identification of saturation point and efficiency levels. A genetic algorithm deployed to identify the optimal budget allocation across media platforms. The result provides a practical media mix for the Sri Lankan advertising industry where advertising planners can allocate budgets more effectively with minimum overspends to achieve higher campaign success. Although the models were learned using a country specific data set, they can be used for similar scale markets. This study contributes to the existing literature by developing an efficient method for media advertising planning.