This thesis investigates the current state of tool wear prediction models in machining, focusing on their limitations in accurately incorporating the complex dynamics of cutting fluids and their industrial applicability. It proposes a comprehensive evaluation framework to classify and evaluate a wide range of models, including empirical, physical, computational, and data-driven models. The study identifies the key limitations and strengths of each model category. It proposes enhancements by integrating advanced fluid dynamics and predictive modeling techniques to improve tool wear predictions' accuracy and industrial applicability. A structured literature review was conducted to investigate and evaluate existing tool wear models and their integration with cutting fluid dynamics. This review included defining search criteria, selecting relevant studies, and assessing their quality and relevance. The study uses thematic analysis and model evaluation frameworks to classify and evaluate the models, leading to the identification of critical limitations and strengths. The literature review and model evaluation findings revealed that empirical models, while simple and quick to implement, showed moderate accuracy and limited fluid dynamics integration. Physical models provided high accuracy in specific conditions but were computationally intensive. Computational models, particularly those using techniques like Finite Element Analysis (FEA) and Computational Fluid Dynamics(CFD), offered detailed insights and high accuracy but required significant computational resources. Data-driven models demonstrated exceptional predictive capabilities and comprehensive fluid dynamics integration but relied heavily on data availability and quality.  The proposed enhancements include introducing non-linear elements into empirical models, incorporating simplified fluid models or empirical correlations into physical models, exploring reduced-order models (ROMs) or surrogate models for computational models, and developing robust data preprocessing and augmentation techniques for data-driven models. These enhancements aim to improve the accuracy and applicability of tool wear models in industrial machining processes, ultimately contributing to more efficient and cost-effective machining operations. The study emphasizes the importance of a systematic and holistic approach to model evaluation and enhancement. Future research should focus on validating these proposed enhancements through empirical studies and real-world applications, ensuring their relevance and robustness in diverse industrial settings. This research offers significant potential to advance tool wear modeling, providing valuable insights for both academia and industry.