The Fram Strait is the primary gateway between the North Atlantic and the Arctic Ocean, where ongoing Atlantification is reshaping hydrographic conditions and marine ecosystems. We applied ML-PhyTAO, a machine learning based retrieval model for Arctic phytoplankton functional types (PFTs), to generate a consistent 28-year (1998–2025) satellite record of total chlorophyll-a (TChla) and major PFT chlorophyll-a concentrations (Chla) in the Fram Strait. Trained with an extensive Arctic phytoplankton pigment dataset and ocean color and environmental products, the model provides robust performance for long-term analyses. Using a dynamic water mass classification based on sea surface temperature and sea ice fraction, the Fram Strait was partitioned into Atlantic Water (AW), Mixed Water (MW), and Polar Water (PW). Distinct seasonal cycles were observed among water masses, with later but more intense phytoplankton blooms in AW. Peak biomass timing advanced by 2–13 days decade⁻¹ for TChla, diatoms, and haptophytes in both AW and MW. Trend analyses revealed declining biomass primarily in MW and the AW–MW transition zone, while haptophytes and dinoflagellates increased within AW. Sea surface salinity, temperature, and sea ice fraction showed stronger and more spatially coherent relationships with PFT variability than modeled nutrient fields, indicating that Atlantification related hydrographic changes are driving shifts in phytoplankton phenology and community composition. This study highlights the unique value of long-term, consistent PFT observations for detecting climate-driven ecosystem change, linking hydrographic variability to community shifts, and supporting future ecosystem and biogeochemical assessments and modelling studies in the rapidly changing Arctic Ocean.