Achieving consistent surface quality in direct laser interference patterning (DLIP) demands real‐time insight into ultrafast laser–material interactions, particularly when structuring complex alloys such as Ti‐6Al‐4V. This work presents a hybrid image‐to‐signal machine learning framework that links offline topography characterization with real‐time sensor data to enable predictive surface quality assessment. Periodic microstructures are fabricated using a picosecond pulsed laser equipped with a two‐beam interference head and an off‐axis photodiode for in situ optical monitoring. Ground‐truth labels are generated from white light interferometry (WLI) images processed in the frequency domain via a 2D fast Fourier transform to extract radial power spectral density profiles. Spectral entropy serves as a quantitative indicator of texture order and enables unsupervised KMeans clustering into acceptable and nonacceptable quality classes. These entropy‐based labels are assigned to the corresponding time‐resolved photodiode signals and laser parameters recorded during fabrication. A supervised 1D convolutional neural network (1D‐CNN) is then trained to predict surface quality using only the sensor data and process inputs. The model achieves a classification accuracy of 90%, demonstrating reliable detection of structural deviations without post‐process metrology. This entropy‐informed, sensor‐driven framework highlights the potential of machine learning for real‐time quality assurance in laser‐based manufacturing systems.