This project applies ML techniques to optimize single- and two-qutrit gates in neutral atom tweezer arrays. We employ Bayesian optimization and reinforcement learning, starting with numerical simulations that incorporate experimentally benchmarked decoherence and loss channels for Ytterbium atoms in optical arrays. Based on these results, we will target experimental implementations in order to demonstrate increased fidelities and enhanced robustness of the various gate implementations.