The Arctic Ocean is experiencing rapid and significant changes due to climate warming, profoundly impacting its physical and biological systems. Phytoplankton, as primary producers, play a crucial role in marine ecosystems and biogeochemical cycles. Monitoring their distribution and abundance is essential for understanding the health of the Arctic marine environment. This study focuses on developing an ensemble machine learning model to predict concentrations of Chl-a of various Phytoplankton Functional Types (PFTs) and Total Chlorophyll-a (TChl-a) in the Arctic Ocean, leveraging data from satellite observations, simulation model outputs and in-situ measurements. The ensemble model combines Gradient Boosting Machine (GBM), Fully Connected Neural Network (FCNN), Random Forest Regression (RFR), and Support Vector Machine (SVM) through a Ridge Regression Ensemble approach. The model was trained by using satellite data and model simulations outputs from Copernicus Marine Service (CMEMS) that were matched with in situ data collected during 1997-2020 and validated using in-situ measurements from the PS131 expedition. The model demonstrates strong predictive capabilities, particularly for diatoms and TChl-a, which are crucial for understanding primary production and nutrient dynamics in the Arctic. Results indicate that the ensemble model performs well in capturing the spatial and temporal distribution of TChl-a and PFTs. The model’s robust performance during the training phase and its ability to generalise to the validation dataset, regardless of its higher variability respect to the training dataset, underscore its potential for large-scale ecological monitoring. The creation of Arctic maps for PFTs and TChl-a provided valuable insights into the spatial distribution of these variables. In the maps created, higher concentrations of diatoms were observed near coastal areas, aligning with known nutrient-rich environments such as river outflows and upwelling zones, particularly along the coasts of northern Europe and northern Asia. Green algae showed a patchy distribution influenced by localised environmental factors, such as variations in light availability and nutrient inputs from specific sources like the Barents Sea and Laptev Sea. Haptophytes exhibited specialised niches in cooler waters, reflecting their ecological roles in regions like the Kara Sea, where lower temperatures and nutrient availability favor their growth. Dinoflagellates were distributed along various coastal regions in northern Europe and northern Asia without a specific area of high concentration, suggesting their adaptability to a range of environmental conditions. In general, the model effectively identified areas of high phytoplankton activity, which are essential for understanding the Arctic marine food web and biogeochemical cycles. Hence, this study demonstrates the potential of using machine learning models for predicting phytoplankton dynamics in the Arctic, offering a robust tool for monitoring and managing marine ecosystems.