Purpose There are no previous studies developing machine learning algorithms in the classification of lumbar sympathetic blocks (LSBs) performance using infrared thermography data. The objective was to assess the performance of different machine learning algorithms to classify LSBs carried out in patients diagnosed with lower limbs Complex Regional Pain Syndrome as successful or failed based on the evaluation of thermal predictors. Methods 66 LSBs previously performed and classified by the medical team were evaluated in 24 patients. 11 regions of interest on each plantar foot were selected within the thermal images acquired in the clinical setting. From every region of interest, different thermal predictors were extracted and analysed in three different moments (minutes 4, 5, and 6) along with the baseline time (just after the injection of a local anaesthetic around the sympathetic ganglia). Among them, the thermal variation of the ipsilateral foot and the thermal asymmetry variation between feet at each minute assessed and the starting time for each region of interest, were fed into 4 different machine learning classifiers: an Artificial Neuronal Network, K-Nearest Neighbours, Random Forest, and a Support Vector Machine. Results All classifiers presented an accuracy and specificity higher than 70%, sensitivity higher than 67%, and AUC higher than 0.73, and the Artificial Neuronal Network classifier performed the best with a maximum accuracy of 88%, sensitivity of 100%, specificity of 84% and AUC of 0.92, using 3 predictors. Conclusion These results suggest thermal data retrieved from plantar feet combined with a machine learning-based methodology can be an effective tool to automatically classify LSBs performance.