This paper presents a quantitative study on the comparative analysis of scientific approaches in Computer Science. The study examines the integration of various scientific disciplines within Computer Science, highlighting the importance of interdisciplinary synergy in addressing complex problems. The research analyzes 300 published articles from 2020 to 2023, focusing on the distribution of scientific approaches, methodologies, and research outcomes. The findings reveal that computational approaches are the most frequently used, followed by experimental, theoretical, and combined approaches. Algorithm development and simulation are the most common methodologies, with computational approaches yielding higher research outcomes compared to theoretical approaches. The study also identifies the type of scientific approach and research methodologies as significant predictors of research impact, explaining 45% of the variance in research outcomes. The results underscore the importance of a holistic approach in Computer Science, emphasizing the integration of diverse scientific fields to foster innovation and solve complex real-world problems. The paper highlights the dynamic nature of scientific disciplines, influenced by cultural and intellectual currents, and the necessity of interdisciplinary collaboration in advancing Computer Science. The study concludes that the integration of different scientific approaches is crucial for addressing the multifaceted challenges of modern science and technology. Future research should focus on exploring interdisciplinary approaches, developing new computational techniques, and considering the ethical and societal implications of technological advancements.This paper presents a quantitative study on the comparative analysis of scientific approaches in Computer Science. The study examines the integration of various scientific disciplines within Computer Science, highlighting the importance of interdisciplinary synergy in addressing complex problems. The research analyzes 300 published articles from 2020 to 2023, focusing on the distribution of scientific approaches, methodologies, and research outcomes. The findings reveal that computational approaches are the most frequently used, followed by experimental, theoretical, and combined approaches. Algorithm development and simulation are the most common methodologies, with computational approaches yielding higher research outcomes compared to theoretical approaches. The study also identifies the type of scientific approach and research methodologies as significant predictors of research impact, explaining 45% of the variance in research outcomes. The results underscore the importance of a holistic approach in Computer Science, emphasizing the integration of diverse scientific fields to foster innovation and solve complex real-world problems. The paper highlights the dynamic nature of scientific disciplines, influenced by cultural and intellectual currents, and the necessity of interdisciplinary collaboration in advancing Computer Science. The study concludes that the integration of different scientific approaches is crucial for addressing the multifaceted challenges of modern science and technology. Future research should focus on exploring interdisciplinary approaches, developing new computational techniques, and considering the ethical and societal implications of technological advancements.