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[Display omitted] • Forensic investigations favour quick and non-destructive identification and discrimination. • Machine learning techniques combined with ATR-FTIR become more prevalent as a way to enhance the precision of the measure. • 220 samples of rubber residues from automobile tyres from different brands had been distinguished. • 15 machine learning approaches have been explored for tyre brand discrimination and type of vehicle identification from rubber residues. • The Extra Tree Classifier outperforms other ML classifiers, in accordance with the outcome of this research. Criminal instances involving collision accidents, hit-and-run incidents, abduction, hostage-taking, and the unauthorised transit of forbidden items generally include evidence involving rubber traces from automobile tyres. These traces can be located on the road surface, in clothing, on the victim(s) themselves, or on items as skid marks following sudden stopping and spinning around. These traces serve as crucial evidence by reducing the range of suspects by revealing linkages between the getaway vehicle, the site of the crime, and the perpetrator through the tyre's brand, producer, or origin. This study offered a way for classifying 220 tyre rubber samples from different brands using various machine learning algorithms in PyCaret in conjunction with rapid and non-destructive ATR-FTIR spectroscopy equipped with diamond crystal. On spectral information from ATR-FTIR, pre-processing tools such as baseline correction, smoothing, derivatization, and normalisation were also implemented prior to machine learning. This approach has the potential to be advantageous for efficiently and non-destructively identifying rubber traces as forensic evidence and for facilitating brand recognition of automobile tyres. [ABSTRACT FROM AUTHOR]