Despite substantial health benefits, smoking cessation attempts have high relapse rates. Neuroimaging measures can sometimes predict individual differences in substance use phenotypes – including relapse – better than behavioral metrics alone. No study to date has compared the relative prediction ability of changes in psychological processes across prolonged abstinence with corresponding changes in brain activity. Here, in a longitudinal design, measurements were made one day prior to smoking cessation, and at 1 and 4 weeks post-cessation (total n=120). Next, we tested the relative role of changes in psychosocial variables vs. task-based functional brain measures predicting time to nicotine relapse up to 12 months. Abstinence was bioverified 4-5 times during the first month. Data were analyzed with a novel machine learning approach to predict relapse. Results showed that increased electrophysiological brain activity during inhibitory control predicted longer time-to-relapse (c-index=0.56). However, reward-related brain activity was not predictive (c-index=0.45). Psychological variables, notably an increase during abstinence in psychological flexibility when experiencing negative smoking-related sensations, predicted longer time-to-relapse (c-index=0.63). A model combining psychosocial and brain data was predictive (c-index=0.68). Using a best-practice approach, we demonstrated generalizability of the combined model on a previously unseen holdout validation dataset (c-index=0.59 vs. 0.42 for a null model). These results show that changes during abstinence – increased smoking-specific psychological flexibility and increased inhibitory control brain function – are important in predicting time to relapse from smoking cessation. In the future, monitoring and augmenting changes in these variables could help improve the chances of successful nicotine smoking abstinence.