Machine learning (ML) is a powerful tool for simulating complex environmental processes, yet barriers to entry remain high for many users of ML-based investigations. In this presentation, we introduce a novel framework designed to lower these barriers by providing a user-friendly, data-driven approach to modeling vegetation dynamics at the leaf scale. Our framework leverages carefully curated datasets of measured leaf-level fluxes, which include information on photosynthesis and stomatal conductance. Aside from being able to select portions of the dataset to apply the ML tests on, users can configure the ML-based recipes to their own liking, including the specification of the number of layers, nodes per layer, and activation functions. By offering predefined ML recipes and a streamlined interface, our tool enables both experienced researchers and newcomers to explore and apply ML-based methods in hydrological and ecological modeling. This work represents a step toward democratizing access to advanced machine learning techniques, which facilitates broader participation in data-driven environmental research. Ultimately, our framework opens new avenues for the exploration of leaf-level processes and contributes to the growing field of AI-ready environmental and climate data.