Marine viruses are key players of ocean biogeochemistry, profoundly influencing microbial community ecology and evolution. Despite their importance, few studies have explored continuous inter-seasonal viral metagenomic time series in marine environments. Viral dynamics are complex, influenced by multiple factors such as host population dynamics and environmental conditions. To disentangle the complexity of viral communities, we developed an unsupervised machine learning framework to classify viral contigs into "chronotypes" based on temporal abundance patterns. Analysing an inter-seasonal monthly time series of surface viral metagenomes from the Western English Channel, we identified chronotypes and compared their functional and evolutionary profiles. Results revealed a consistent annual cycle with steep compositional changes from winter to summer and steadier transitions from summer to winter. Seasonal chronotypes were enriched in potential auxiliary metabolic genes of the ferrochelatases and 2OG-Fe(II) oxygenase orthologous groups compared to non-seasonal types. Chronotypes clustered into four groups based on their correlation profiles with environmental parameters, primarily driven by temperature and nutrients. Viral contigs exhibited a rapid turnover of polymorphisms, akin to Red Queen dynamics. However, within seasonal chronotypes, some sequences exhibited annual polymorphism recurrence, suggesting that a fraction of the seasonal viral populations evolve more slowly. Classification into chronotypes revealed viral genomic signatures linked to temporal patterns, likely reflecting metabolic adaptations to environmental fluctuations and host dynamics. This novel framework enables the identification of long-term trends in viral composition, environmental influences on genomic structure, and potential viral interactions.