The present manuscript tested an automated analysis sequence to provide a decision support system to track the OCP synthesis from α-TCP over time. Initially, the XRD and FTIR signals from a hundredfold scaled-up hydrolysis of OCP from α-TCP were fused and modeled by the curve fitting based on the significantly established maxima from the literature and nine features extracted from the fitted shapes. Afterward, the analysis sequence enclosed the machine learning techniques for feature ranking, spatial filtering, and dimensionality reduction to support the automatic recognition of the synthesis stages. The proposed analysis pipeline for OCP identification might be the foundation for a decision support system explicitly targeting OCP synthesis. Future projects will exploit the suggested methodology for pinpointing the OCP production over time (including the intermediary phases present in the OCP formation) and for evaluating whether biological variables might be merged with biomaterial properties to build a unified model of tissue response to the implant.