BACKGROUND: Modifiable lifestyle behaviors significantly influence the risk of cognitive impairment. However, the cumulative effects of multidimensional lifestyle profiles on cognitive function remain poorly understood, as most studies examine individual lifestyle behaviors in isolation. This study aimed to identify distinct profiles of individuals based on healthy lifestyle behaviors and to examine associations between these profiles and cognitive function in older Chinese adults. METHODS: We used a prospective cohort, including 5381 participants of the Chinese Longitudinal Healthy Longevity Survey (CLHLS) between 2008 and 2018, aged 65 years and older with normal cognition at baseline. Questionnaires were used to collect self-reported data on healthy diet, sleep quality, physical activities, cognitive activities, and social activities. Repeated measures of the Mini-Mental State Examination (MMSE) were utilized to assess cognitive function. Latent Profile Analysis (LPA) was conducted to identify profiles characterized by similar characteristics of lifestyle behaviors. The resultant profiles, were then used to further explore associations with cognitive function using cox proportional hazard regression and linear mixed models. RESULTS: During a 10-year follow-up period, 2017 (37.5%) out of 5381 participants developed cognitive impairment. Three latent profiles were identified: (1) "active engagement" (n = 347, 6.4%); (2) "moderate engagement" (n = 627, 11.7%); and (3) "negative engagement" (n = 4407, 81.9%). Compared to negative engagement, the active profile had the lower risk of cognitive impairment (HR = 0.693, 95% CI: 0.553-0.868), longer years to develop cognitive impairment (mean = 7.818, range: 6.701, 8.935) and slower rate of annual cognitive decline (0.407 points per year). Similarly, the moderate engagement profile had the lower risk of cognitive impairment (HR = 0.781, 95% CI: 0.664-0.919), longer years to develop cognitive impairment (mean = 7.541, 95%CI: 6.464, 8.619) and slower rate of annual cognitive decline (0.297 points per year) compared to negative profile. Subgroup analysis revealed that there were no significant differences observed across any of the subgroups, including age, gender, year of schooling, marital status, residence, live alone, family economic status. CONCLUSIONS: These findings imply the likelihood of an inverse correlation between the levels of engagement in healthy lifestyle behavior and the risk of cognitive impairment. Even adopting a few healthy lifestyle habits is superior to none at all, underscoring the value of lifestyle modifications for cognitive health.