This paper explores an interdisciplinary approach to investment decision-making by integrating Python programming, logical frameworks, mathematical models, and algorithmic strategies. Effective investment strategies in a data-driven financial landscape require computational efficiency, predictive modeling, and risk assessment. Python facilitates data processing and simulation, while mathematical models support portfolio optimization and risk management. Logical frameworks ensure consistency in decision rules, and advanced algorithms, including machine learning and dynamic programming, enhance adaptability to market changes. This study presents a systematic methodology for developing transparent, scalable, and efficient investment strategies. By leveraging artificial intelligence and computational tools, the proposed framework equips investors and analysts with practical insights to navigate financial complexities. The findings highlight the synergy between human reasoning and technology in investment decision-making, offering valuable resources for professionals and researchers seeking innovative approaches to financial analysis.