Electrochemical C-H oxidations are tunable and cost-effective transformations for streamlining conversion of hydrocarbons to decorated oxidized molecules. As synthetic chemists actively expand the scope of this field and discover new chemical reactions, the selection of reactive substrates and the subsequent optimization of synthesis parameters, while often guided by fundamental chemical principles and hypotheses, still require extensive empirical condition screening and remain resource-consuming. Consequently, smart workflows that bypass the traditional trial-and-error approach are essential to meet the increasing demand from chemists to navigate reactivity space and expedite new reaction discovery. Recent advancements in machine learning (ML) have shown promising potential in reactivity prediction and optimization of organic reactions. Simultaneously, large language models (LLMs) have gained attention in chemical research, offering novel interfaces with chemical data and assisting chemists-particularly those less experienced with digital tools-in customizing their digital workflows through intuitive natural language prompts. In essence, ML provides a toolbox for specific tasks in chemical research and LLMs serve as meta-tools that enhance the accessibility of these computational tools, bridging the gap between digital proficiency and chemical expertise. Consequently, the integration between the mathematical rigor of ML and the language understanding and domain-specific knowledge of LLMs can make data-driven methodologies accessible to a broader community of chemists. This motivates us to explore the following underexplored questions in this evolving field: What are the capabilities of LLMs in synthetic electrochemistry? How can LLM be integrated with ML to reliably expedite reaction discovery? Herein, we demonstrate the synergistic potential of ML and LLMs to advance the exploration and optimization of electrochemical C-H oxidation reactions. For the ML aspect, our approach aims to address two fundamental questions: (1) Which substrates are suitable for electrochemical oxidation? and (2) What synthesis conditions give optimal results? By leveraging both literature data and rapid screening experimental results, we train models to predict reactivity and selectivity for C(sp )-H oxygenation, enabling in silico screening of chemical entities for initial hits. Subsequently, for these selected substrates of interest, an active learning protocol designed for reaction yield optimization was applied to iteratively and rapidly navigate the search space to identify optimal synthesis conditions. Throughout this study, we also illustrate the versatility of LLM agents-to be a tool (e.g. extracting knowledge from literature), to use tools (e.g. employing ML and liquid handler for synthesis), and to create tools (e.g. generating custom Python code for prediction and analysis). As such, this multifaceted utility-enhancing workflow efficiency and intelligence-serves as a practical example of the application of LLMs in streamlining chemical research processes and underscores their potential to accelerate scientific discovery for synthetic chemists.