In the era of data-driven decision-making, selecting appropriate nonlinear modeling techniques is critical for building robust and interpretable predictive systems. While both Takagi-Sugeno (T-S) fuzzy models and Back-Propagation (BP) neural networks are well-established universal approximators in the data science domain, their fundamentally different structural characteristics lead to varied performance across application scenarios. This paper presents a systematic comparative study of these two modeling approaches through a series of simulation experiments designed to reflect key tasks in predictive analytics, including static function approximation, dynamic system modeling and forecasting, and real-time state tracking of time-varying systems. By evaluating performance across multiple dimensions modeling accuracy, robustness to noise, and adaptability to temporal dynamics, this work provides actionable insights into the practical strengths and limitations of each model type. The results show that T-S fuzzy models offer superior accuracy in clean, stable environments, while BP neural networks demonstrate strong resilience to noise and generalization ability in uncertain conditions. Additionally, T-S fuzzy models exhibit higher adaptability in real-time, dynamic contexts, making them valuable for time-sensitive predictive applications. This study contributes to the broader data science community by offering a structured framework for model selection based on application-specific demands, helping practitioners and researchers alike to optimize predictive modeling strategies for complex, nonlinear systems.