This paper presents a comparative analysis of scientific approaches in Computer Science, emphasizing the interdisciplinary nature of the field. The study, conducted by Felix Kruger, Zabenaso Queen, Ocean Radelva, and Neil Lawrence, explores the integration of theoretical frameworks with practical applications. The authors highlight the historical evolution of Computer Science, noting the emergence of numerous sub-disciplines that increasingly communicate and overlap. They argue that a holistic view, acknowledging the interdependencies among various scientific disciplines, is essential for addressing complex, multifaceted challenges. The paper delves into the foundational concepts of Computer Science, such as recursion, and their practical applications in areas like artificial intelligence, machine learning, cybersecurity, and data analytics. It also examines the dynamic nature of scientific disciplines, influenced by cultural and intellectual currents, and the importance of interdisciplinary collaboration in fostering innovation. The research design is quantitative, using a systematic sampling method to select 300 research articles from top-tier journals and conference proceedings. The data is analyzed using descriptive and inferential statistics, including chi-square tests, ANOVA, and regression analysis. The results indicate that computational approaches are the most frequently used, followed by experimental, theoretical, and combination approaches. Algorithm development and simulation are the most common methodologies, with computational approaches yielding significantly higher research outcomes. The study concludes that computational methods, characterized by their ability to handle large datasets and perform complex simulations, are crucial for addressing contemporary scientific challenges. The findings suggest that the type of scientific approach and specific research methodologies are significant predictors of research impact. The paper recommends further exploration of interdisciplinary approaches, the development of new computational techniques, and the consideration of ethical and societal implications of technological advancements.This paper presents a comparative analysis of scientific approaches in Computer Science, emphasizing the interdisciplinary nature of the field. The study, conducted by Felix Kruger, Zabenaso Queen, Ocean Radelva, and Neil Lawrence, explores the integration of theoretical frameworks with practical applications. The authors highlight the historical evolution of Computer Science, noting the emergence of numerous sub-disciplines that increasingly communicate and overlap. They argue that a holistic view, acknowledging the interdependencies among various scientific disciplines, is essential for addressing complex, multifaceted challenges. The paper delves into the foundational concepts of Computer Science, such as recursion, and their practical applications in areas like artificial intelligence, machine learning, cybersecurity, and data analytics. It also examines the dynamic nature of scientific disciplines, influenced by cultural and intellectual currents, and the importance of interdisciplinary collaboration in fostering innovation. The research design is quantitative, using a systematic sampling method to select 300 research articles from top-tier journals and conference proceedings. The data is analyzed using descriptive and inferential statistics, including chi-square tests, ANOVA, and regression analysis. The results indicate that computational approaches are the most frequently used, followed by experimental, theoretical, and combination approaches. Algorithm development and simulation are the most common methodologies, with computational approaches yielding significantly higher research outcomes. The study concludes that computational methods, characterized by their ability to handle large datasets and perform complex simulations, are crucial for addressing contemporary scientific challenges. The findings suggest that the type of scientific approach and specific research methodologies are significant predictors of research impact. The paper recommends further exploration of interdisciplinary approaches, the development of new computational techniques, and the consideration of ethical and societal implications of technological advancements.