Quantitative processing of optoacoustic angiograms is an important task, the solution of which will potentially enable the early diagnosis of vascular diseases. The aim of this study is to refine and conduct biomedical validation of a software tool for the analysis of optoacoustic angiograms, focusing on the application of machine learning methods. The work was conducted on an open dataset containing three-dimensional optoacoustic angiograms of an experimental animal (mouse) in three temperature conditions: cold temperature (16 °C), room temperature (23 °C), and body temperature (30 °C), as well as a dataset with basic vascular features obtained by processing using Amira/Avizo (Thermo Fisher Scientific), a general-purpose software for visualization and analysis of scientific and industrial data. Various vascular features missing from previous work were developed and calculated, after which basic methods of unsupervised/supervised clustering and supervised classification were applied to determine different temperature conditions of vessel segments. Supervised classification methods demonstrated high overall accuracy: CatBoost — 98.9%, SGDClassifier — 95.7%, and logistic regression — 99.7%. The results are consistent with existing descriptions of vascular changes during temperature tests. The applied methodology is universal, meaning with minor modifications it can be adapted to patients. Therefore, the results of this study may potentially improve the diagnosis of vascular pathologies.