Background: Some cancer survivorship programs incorporate components of healthy lifestyle behavior modification. We evaluated the role that various clinical variables and smoking habits play in predicting which cancer survivors are more likely to quit smoking. Such knowledge may help with resource allocation within these programs. Methods: We focused on lung cancer (LC) and head and neck cancer (HNC) patients as these have the highest active smoking rates among all cancer sites at Princess Margaret Cancer Centre. Patients from 2006-12 completed questionnaires at diagnosis (baseline) and follow-up (median 2 years apart) that assessed smoking status. Baseline clinical and demographic information was obtained. Multivariate logistic regression analysis evaluated the association of smoking and clinical variables at diagnosis to subsequent smoking cessation. Predictive models were assessed for their discriminatory capabilities (concordance-index or area under the curve, AUC). Results: In this cohort, 261/731 LC and 145/450 HNC patients smoked at diagnosis; subsequent overall quit rates were 69% and 50% respectively. Univariate factors associated with smoking cessation included having LC (p = 0.001), being married (p = 0.02), having at least completed secondary school (p = 0.049), having less cumulative smoking (pack-years; p = 0.004), and having adequate social support (p = 0.009). In multivariate modeling, fewer pack-years, having LC and being married remained significant and this predictive model was associated with moderate predictive ability (AUC 0.68 [95% CI: 0.62-0.73]. However, the addition of either SHS household exposure (AUC 0.76 [0.71-0.81]) or spousal smoking (AUC 0.77 [0.71-0.82]) further improved the predictive model. The addition of SHS variables in other exploratory predictive models of smoking cessation improved those models in a similar manner. Similar improvements in prediction were seen in subgroup analysis of LC and HNC. Conclusions: SHS exposure significantly improves the predictive abilities to determine which patients who smoked at their cancer diagnosis would subsequently quit. Cessation programs may benefit from allocating resources accordingly.