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Laser powder bed fusion (PBF‐LB) is an additive manufacturing (AM) technology for producing complex geometry parts. However, the high cost of post‐processing coarse as‐built surfaces drives the need to control surface roughness during fabrication. Prior studies have evaluated the relationship between process parameters and as‐built surface roughness, but they rely on forward models using trial‐and‐error, regression, and data‐driven methods based only on areal surface roughness parameters that neglect spatial surface characteristics. In contrast, this study introduces, for the first time, an inverse data‐centric framework that leverages machine learning algorithms and an experimental dataset of Inconel 718 as‐built surfaces to predict the PBF‐LB process parameters required to achieve a desired as‐built roughness. This inverse model shows a prediction accuracy of ≈80%, compared to 90% for the corresponding forward model. Additionally, it incorporates deterministic surface roughness parameters, which capture both height and spatial information, and significantly improves prediction accuracy compared to only using areal parameters. The inverse model provides a digital tool to process engineers that enables control of surface roughness by tailoring process parameters. Hence, it establishes a foundation for integrating surface roughness control into the digital thread of AM, thereby reducing the need for post‐processing and improving process efficiency.