Plasma modeling is central to the design of nuclear fusion reactors, yet simulating collisional plasma kinetics from first principles remains a formidable computational challenge: the Vlasov–Maxwell–Landau (VML) system describes six-dimensional phase-space transport under self-consistent electromagnetic fields together with the nonlinear, nonlocal Landau collision operator. A recent deterministic particle method for the full VML system (Bailo-Carrillo-Hu, 2024) estimates the velocity score function via the blob method, a kernel-based approximation with O(N^2) cost. In this work, we replace the blob score estimator with score-based transport modeling (SBTM), in which a neural network is trained on-the-fly via implicit score matching at O(N) cost. We prove that the approximated collision operator preserves momentum and kinetic energy, and dissipates an estimated entropy. On three canonical benchmarks – Landau damping, two-stream instability, and Weibel instability – SBTM is more accurate than the blob method, achieves correct long-time relaxation to Maxwellian equilibrium where the blob method fails.