We have successfully (1) developed and validated machine learning models for predicting reactivity and site selectivity in electrochemical C-H oxidation reactions, achieving high accuracy, (2) created a cost-effective, rapid screening electrochemical platform to facilitate rapid data generation and reactivity screening, (3) leveraged large language models to semantically analyze scientific literature and generate ML code, significantly lowering the barrier for chemists to utilize ML tools, and (4) employed a synergistic approach combining ML and LLMs to iteratively refine synthesis conditions, leading to highyield optimizations for selected substrates and a 1071 electrochemical reaction dataset. At a fundamental level, large language models can be perceived as motivated learners, while their foundational versions might only grasp the basics of chemistry. Importantly, incorporating machine learning models and coherent human instruction significantly enhances their proficiency in a range of chemistry-related tasks. This integration has shown potential in streamlining laboratory work and making digital tools more accessible to chemists with limited coding experience. Despite these promising results, there is still a long journey ahead. Opportunities for improvement include designing more sophisticated human-AI interaction frameworks, better interfacing with other digital tools, and expanding the knowledge base with external sources. Besides, fine-tuning LLMs to better align with human instructions can reduce uncertainty and enhance goal alignment. This integrated AI-powered methodology not only streamlines the traditional trial-and-error process but also offers a robust and generalizable pathway for potentially expanding to a wider range of reaction types and conditions and enable further automating the discovery process by coupling with commercial molecule databases. Overall, this work underscores the potential of human-AI collaboration, combining the strengths of LLMs and ML, to advance synthetic chemistry research.