Abstract Protoplanetary disks (PPDs) are sites of vigorous hydrodynamic processes, such as accretion and outflows, and ultimately establish the conditions for the formation of planets. The properties of disk outflows are often inferred through the analysis of forbidden emission lines. These lines contain multiple overlapping components, tracing different emission regions with different processes that excite them: a high-velocity component (tracing a jet), a broad low-velocity component (LVC; tracing inner disk wind), and a narrow LVC (tracing the outer disk wind). They are also heavily contaminated by background spectral features. All of these challenges call into question the traditional approach of fitting Gaussian components to the line profiles and cloud the physical interpretation of those components. We introduce a novel statistical technique to analyze emission lines in PPDs. Simulation-based inference is a computationally efficient machine-learning technique that produces posterior distributions of the parameters (e.g., magnetic field, radiation sources, and geometry) of a representative wind model when given a spectrum without any prior assumption about line shapes (e.g., symmetry). In this pathfinder study, we demonstrate that this technique indeed accurately recovers the parameters from simulated spectra without noise and background. Future work will provide an analysis of the observed spectra.