Next-generation discoveries in quantum computing and particle physics require large-scale data generation and classification strategies, with theoretical support for its precision and accuracy. While such needs are empirically addressed by state-of-the-art Artificial Intelligence (AI) and Machine Learning (ML), theoretical support for robust data generation via reliable algorithms, that meet scientific uncertainty quantification benchmarks, are still majorly lacking. The performance of AI/ML hinges on an ability to classify and discard data features as per a hierarchy of their relevance, leading to emergent behaviours e.g. neural scaling laws, phase transitions, and universality – mirroring phenomena in theoretical physics. I will present a few directions where tools of theoretical physics, such as statistical physics, field theory, and renormalization (RG) etc., – leveraged to study AI/ML at a microscopic level – have predicted macroscopic model behaviour e.g. out-of-distribution generalization, uncertainty quantification, and neural scaling laws, beyond the scope of traditional statistical means. Going beyond improved AI/ML explainability and robustness, these works enable interpretation of field theories in terms of Neural Network statistics, quantify model error propagation as RG flows, and provide theoretical benchmarking via neural scaling laws for AI/ML performance on data from Rydberg atom arrays.