Flowering time is an important trait affecting regional adaptation and yield stability in rapeseed (Brassica napus). To dissect the genetic basis of flowering time and vernalization-type differentiation in B. napus, this study integrated genome-wide SNPs, flowering-time phenotypes, vernalization types, and geographic origin information to establish a candidate QTL discovery framework combining genome-wide association study (GWAS) and machine learning. Based on 8,387,529 high-quality SNPs, GWAS was performed using the FarmCPU model under two covariate settings: a PC-corrected model and a PC + vernalization type-corrected model. These two models identified 32 and 27 suggestive SNPs, respectively. In parallel, ExtraTreesRegressor was used to estimate the feature contribution of SNPs to flowering-time prediction, leading to the identification of 18,854 ML_delta10SD high-contribution SNPs, which were further merged into 997 ML-derived candidate QTL intervals, hereafter referred to as ML-QTLs. By integrating GWAS-derived QTLs and ML-QTLs at the interval level, 34 GWAS- and ML-supported candidate regions were prioritized. Candidate gene annotation revealed that these regions contained genes related to flowering regulation, including FLC, FRIGIDA-like, VIN3-like, SOC1, ELF3, CONSTANS-like, APETALA1-like, and genes involved in the gibberellin pathway. Top-SNP genotypic effect analysis and multi-SNP genotype combination analysis further showed that genetic variation within representative candidate regions was significantly associated with flowering-time differences and vernalization-type differentiation. Predictive modeling indicated that candidate markers selected by GWAS and machine learning showed predictive potential within the present population. Overall, this study prioritized a set of GWAS- and ML-supported candidate regions and putative candidate genes associated with flowering time in B. napus from the complementary perspectives of statistical association and machine learning-based predictive contribution, providing candidate resources for future marker development, functional validation, and adaptive improvement in rapeseed.