The integration of Large Language Models (LLMs) into various stages of machine learning (ML) workflows is emerging as a significant trend, particularly in automating and enhancing data and feature engineering, model selection, hyperparameter optimization, and workflow evaluation. This shift is driven by LLMs' capacity for language understanding, reasoning, and generation, which streamlines the ML process and improves model performance. Additionally, there is a growing focus on developing AI-powered systems for real-time health monitoring and forecasting, such as glucose level prediction, which require efficient models that can operate on edge devices. These systems often employ hybrid attention models and knowledge distillation techniques to handle complex, non-linear data and reduce computational complexity. Another notable development is the exploration of novel post-training paradigms for foundation models, such as verifier engineering, which aims to enhance model capabilities through automated verification and feedback mechanisms. Furthermore, LLMs are being increasingly applied to combinatorial optimization problems in engineering, leveraging their contextual knowledge to improve solution quality and convergence rates in complex scenarios. This approach represents a new frontier in optimizing real-world engineering challenges. Overall, the field is moving towards more integrated, context-aware, and efficient ML solutions, driven by advancements in LLMs and AI-driven health systems.