Self-optimizing HTE throughput platforms require inline and/or online characterization, data analysis and processing for rapid optimization of organic reactions. Chromatographic (i.e. HPLC, GC) and spectroscopic (e.g. NMR, FTIR, UV-Vis, Raman) characterization methods are commonly used in real-time reaction monitoring. To quantify the products of a chemical reaction a calibration curve is required before the optimization campaign. The following sequential steps are typically employed to refine raw data into actionable inputs for building ML models for optimization. (i) Extraction and categorization of appropriate spectra; (ii) Fitting of spectral peaks utilizing predefined functional models, alongside deconvolution of overlapping signals; (iii) Consolidation of extracted peak information and generation of relevant data plots and (iv) extracting the relevant information and formatting into input data for ML models. A recent review from Felpin et al. highlighted the selection of in-line/online analytical tools that can be integrated into flow reactors for the monitoring of chemical reactions. In the current review, we focus on the HT data processing that complements the HTE platforms for rapid optimization of organic reactions. Although multivariate data analysis has been adopted in analytical chemistry frequently for rapid data processing, the availability of relevant open-source code is relatively low . Consequently, the development of open-source code for data processing become interest to the scientific community extensively.