The modeling and simulation of biological systems have become indispensable tools for understanding complex phenomena within living organisms. Among the diverse modeling approaches, two prominent methodologies have emerged: Agent-Based Models (ABMs) and Differential Equation Models (DEMs). This research paper presents a comprehensive comparative study between these two modeling paradigms, with a focus on their applicability, advantages, and limitations in the context of biological systems. The first section of the paper introduces the fundamental principles of ABMs and DEMs, elucidating their underlying assumptions and mathematical foundations. ABMs are agent-centric models, where individual agents interact with each other and their environment to create emergent behaviors at the macroscopic level. In contrast, DEMs describe the system using differential equations, which provide a continuous representation of the variables and their rate of change over time. Next, the paper delves into the strengths and weaknesses of each modeling approach concerning the representation of biological processes. ABMs excel in capturing the heterogeneity and spatial aspects of living systems, allowing for the simulation of complex interactions among various entities. They are particularly suitable for modeling phenomena such as population dynamics, immune responses, and social behavior. However, ABMs might require significant computational resources and can be challenging to calibrate due to their inherent stochastic nature. On the other hand, DEMs offer mathematical rigor and efficiency in analyzing deterministic biological systems. Their deterministic nature facilitates parameter estimation and allows for the application of well-established mathematical tools, making them ideal for modeling biochemical reactions, gene regulatory networks, and physiological processes. Nevertheless, DEMs may struggle to represent spatial effects and individual variability, limiting their suitability for certain biological scenarios. The third section of the paper presents case studies where ABMs and DEMs are applied to model specific biological phenomena. By comparing the outcomes of these studies, we gain valuable insights into the strengths and limitations of each approach within different contexts. These case studies cover a wide range of biological systems, including cellular behavior, disease spread, and ecological interactions. Lastly, the paper discusses the integration of ABMs and DEMs, where hybrid models combining both methodologies are used to leverage their complementary strengths. Such hybrid models attempt to strike a balance between computational efficiency and spatial resolution, offering a promising avenue for future research in biological modeling and simulation. In conclusion, this research paper highlights the importance of selecting appropriate modeling approaches for specific biological questions. While ABMs and DEMs have their distinct advantages, their integration and synergistic utilization hold great potential for advancing our understanding of complex biological systems. Ultimately, this study aims to provide researchers and practitioners with a comprehensive guide to choose the most suitable modeling strategy based on the nature of their biological investigation