Abstract: Financial literacy is an important life skill that helps individuals manage money, savings, investments, budgeting, and financial decision-making effectively. In the digital era, the integration of technology and mathematics has created new opportunities for improving financial education through practical and analytical learning methods. This study examines the role of Python programming and mathematical modeling in financial literacy skill development using secondary data analysis. The research is based on data collected from educational reports, financial literacy surveys, and publicly available datasets. Mathematical and statistical techniques such as percentage analysis, correlation, simple interest, compound interest, and regression analysis are used to understand financial behavior and literacy patterns. Python tools including Pandas, NumPy, Matplotlib, and Seaborn are applied for data analysis, visualization, and interpretation. The study highlights how Python-based practical activities can improve students’ understanding of financial concepts through real-world applications. The use of graphs, charts, and computational models helps learners develop analytical thinking and problem-solving skills. The findings indicate that combining mathematical modeling with Python programming enhances financial awareness and supports skill-based education. The study concludes that technology-integrated mathematics education can play a significant role in strengthening financial literacy and preparing learners for practical financial decision-making in daily life. The research also emphasizes the importance of digital tools and data-driven learning approaches in modern education systems.