Aim: This study evaluated the use of machine learning in leveraging drug ADME data to develop a novel anticholinergic burden (AB) scale and compared its performance to previously published scales. Methods: Experimental and in silico ADME data were collected for antimuscarinic activity, blood-brain barrier penetration, bioavailability, chemical structure and P-gp substrate profile. These five ADME properties were used to train an unsupervised model to assign anticholinergic burden scores to drugs. The performance of the model was evaluated through 10-fold cross-validation and compared with the clinical ACB scale and non-clinical ATS scale which is based primarily on muscarinic binding affinity. Results: In silico software (ADMET predictor ®) used for screening drugs for their blood-brain barrier (BBB) penetration correctly identified some drugs that do not cross the BBB. The mean AUC for the unsupervised and ACB scale based on five selected features was 0.76 and 0.64 respectively. The unsupervised model agreed with the ACB scale on the classification of more than half of the drugs (n=49 of m=88) and agreed on the classification of less than half the drugs in the ATS scale (n=12/25). Conclusion: Our findings suggest that the commonly used ACB scale may misclassify certain drugs due to their inability to cross the BBB. On the other hand, the ATS scale would misclassify drugs solely depending on muscarinic binding affinity without considering ADME properties. Machine learning models can be trained on these features to build classification models that are easy to update and have greater generalizability.