Surface polishing by laser remelting (SP-LRM) is a novel, versatile, high-speed, and low-cost advanced manufacturing technology for producing high-quality surface finishes. The process utilizes a high-power laser that delivers a large amount of instantaneous energy melting a superficial thin layer of material to a molten state. This allows the melt pool to flow driven by thermocapillary and surface tension forces. The target of the process is to melt, reallocate, and resolidify surface peaks into valleys in order to yield a low surface roughness (Sa). SP-LRM, complexity arises from instabilities occurring during laser-material interactions, resulting from non-linear thermodynamics, initial surface topography, overheating, abrupt changes in laser path trajectories involving acceleration and deceleration, and various other factors. These process instabilities significantly affect the attainment of a desired smooth final surface. Presently, the identification of anomalies resulting from a specific set of laser parameters in laser remelting (LRM) is performed offline by assessing the surface topography of LRM using optical profilometer and correlating it with surface non-uniformities that are indicative of process instabilities. This study streamlines the anomaly detection process and identifies the presence of irregularities using an unsupervised clustering machine learning (ML) technique, specifically K-means clustering. During the laser remelting (LRM) process, a high-speed near-infrared (NIR) camera captures relative thermal emission images, which are then classified into a minimum of three clusters using the K-means algorithm. These clusters correspond to positive and negative axial laser beam positions, indicating shifts in the laser spot on the image, and the stability states of laser-material interactions. Preliminary findings show promising results in the employment of artificial intelligence (AI) to enhance LRM as a conventional industrial technology for polishing and structuring tooling surfaces.