A new machine learning pipeline at Shahid Beheshti University, in collaboration with AriaQuanta Quantum Co and Shahid Sattari University of Aeronautical Sciences and Technology, accelerates the creation of Gottesman-Kitaev-Preskill (GKP) states, key resources for strong photonic quantum computing. Mohammad Amin Khanpour and Hossein Davoodi Yeganeh, alongside colleagues, present a two-stage surrogate model that accurately predicts the performance of Gaussian Boson Sampling circuits for GKP state generation, bypassing computationally expensive hafnian calculations. Achieving 90.0% GKP-detection accuracy and a 23.7 percentage-point improvement over existing methods, the approach sharply reduces the simulation burden by approximately 90%, representing a substantial step towards practical, all-photonic quantum computation. Machine learning pipeline unlocks high-fidelity GKP states for scalable quantum computation GKP-detection accuracy now reaches 90.0%, a 23.7 percentage-point leap beyond previous methods. This enables the creation of high-fidelity Gottesman-Kitaev-Preskill (GKP) states, essential for strong photonic quantum computing, which were previously unattainable due to computational limitations. Reaching this level of accuracy crosses a key threshold for error correction, as GKP states require a fidelity of at least 0.90 to meaningfully protect against logical errors in quantum calculations. A new machine learning pipeline sharply reduces the computational burden of simulating Gaussian Boson Sampling (GBS) circuits, a technique for generating these non-Gaussian states, by approximately 90%. Previously, evaluating a single circuit configuration could take five minutes on a workstation. At 90.0%, accuracy in detecting Gottesman-Kitaev-Preskill (GKP) states represents a 23.7 percentage-point increase over previous techniques. These states are important for building stable photonic quantum computers, enabling more reliable encoding of quantum information and protection against errors. The improvement was realised through a new machine learning pipeline that predicts optimal circuit configurations for Gaussian Boson Sampling (GBS), a method of generating these complex states using light. GBS utilises squeezed-state sources, linear interferometers and photon-number-resolving detectors. The pipeline reduces the computational time