Abstract Background: We can study how fast our biological aging clocks tick by calculating the difference (i.e., age-gaps) between machine learning estimations of biological age and chronological age. While this approach has been increasingly used to study various aspects of aging, few had applied this approach to study cognitive and physical age-gaps; not much is known about the behavioral and neurocognitive factors associated with these age-gaps. In the present study, we examined these age-gaps in relation to behavioral phenotypes and mild cognitive impairment (MCI) among community-dwelling older adults. Methods: Participants (N=822, Age mean =67.6) were partitioned into equally-sized training and testing samples. Cognitive and physical age-prediction models were fitted using nine cognitive and eight physical fitness test scores, respectively, within the training samples, and subsequently used to estimate cognitive and physical age-gaps for each subject in the testing sample. These age-gaps were then compared among those with and without MCI, and correlated with 17 behavioral phenotypes in the domains of lifestyle, well-being, and attitudes. Results: Across 5,000 random train-test split iterations, we showed that older cognitive and physical age-gaps were significantly associated with MCI (versus cognitively normal) and worse outcomes across several well-being and attitude-related measures. Both age-gaps were also significantly correlated with each other. Conclusions: These results suggest accelerated cognitive and physical aging were linked to worse well-being and more negative attitudes about the self and others, and reinforce the link between cognitive and physical aging. Importantly, we have also validated the use of cognitive age-gaps in the diagnosis of MCI.