Wearable biosensors allow continuous monitoring of physiological and behavioral signals associated with substance use disorders. Nevertheless, generating clinically meaningful insights requires novel machine learning techniques capable of handling high-velocity, noisy data streams. This paper surveys existing algorithms proposed for detecting substance use from wearables, highlighting key trends such as deep learning approaches and personalization. Ongoing challenges related to ground truth labeling, model interpretation, and computational efficiency are also discussed. The advent of 5G/6G networks is poised to transform this field by facilitating coordinated sensing across multiple wearables and enabling advanced on-device analytics. This work consolidates progress in wearable analytics for substance use monitoring and identifies open problems to steer future research. An experimental evaluation using wearable data from 20 participants demonstrates that a personalized CNN model achieved the best performance in detecting cannabis use events, boasting 89% precision, 83% recall, and a 0.94 AUROC score, outperforming classical machine learning approaches such as random forests.