Recent research has identified higher prevalence of offending behavior in patients with comorbid schizophrenia spectrum disorder (SSD) and substance use disorder (SUD) compared to patients with SSD only and to the general population. However, findings on the subgroup of patients with SUD, SSD and offending behavior in forensic psychiatric care are scarce and inconsistent. The present study used machine learning to uncover more detailed characteristics of offender patients in forensic psychiatric care with comorbid SSD and SUD. Using machine learning algorithms, 370 offender patients (91.6 % male, mean age of M = 34.1, SD = 10.2) and 558 variables were explored in order to build three models to differentiate between no substance use disorder, cannabis use disorder and any other substance use disorder. To counteract the risk of overfitting, the dataset was split, employing variable filtering, machine learning model building and selection embedded in a nested resampling approach on one subset. The best model was then selected and validated on the second data subset. Distinguishing between SUD vs. no drug use disorder yielded models with an AUC of 70 and 78. Variables assignable to demographics, social disintegration, antisocial behavior and illness were identified as most influential for the distinction. The model comparing cannabis use disorder with other substance use disorders provided no significant differences. From a clinical perspective, offender patients suffering from schizophrenia spectrum and comorbid substance use disorder seem particularly challenging to treat, but initial differences in psychopathology will dissipate over inpatient treatment. Our data suggest that offender patients may benefit from appropriate treatment that focuses on illicit drug abuse to reduce criminal behavior and improve social integration.