Complex non-linear relationships exist between the permafrost thermal state, active layer thickness, and terrestrial carbon cycle dynamics. In Arctic and boreal Alaska, large uncertainties characterizing the spatiotemporal rate, magnitude, and extent of permafrost degradation and the permafrost carbon feedback, with an increasing recognition of the importance of abrupt thaw mechanisms. The challenges of monitoring sub-surface phenomena, such as soil temperature and soil moisture profiles, with remote sensing technology further complicates the issue. There is an urgent need to understand how and to what extent permafrost degradation destabilizes the carbon balance in Alaska and characterizes the feedbacks involved. In this research, we employ our artificial intelligence (AI)-driven model GeoCryoAI to quantify permafrost thaw dynamics and greenhouse gas (GHG) emissions in Alaska. The GeoCryoAI model uses a hybridized multimodal deep learning architecture of stacked convolutionally layered memory-encoded bidirectional recurrent neural networks to simultaneously ingest and analyze in situ measurements, remote sensing observations, and process-based modeling outputs with disparate spatiotemporal sampling and data densities. Evaluation of naïve persistence, teacher forcing, and time-delayed GeoCryoAI simulations yielded promising results with the following error metrics (RMSE) for active layer thickness (ALT), carbon dioxide (CO₂), and methane (CH₄) respectively: 1.997cm, 1.327cm, 1.007cm [1969-2022]; 1.906µmolCO₂m^-2s^-1, 0.697µmolCO₂m^-2s^-1, 0.213µmolCO₂m^-2s^-1 [2011-2022]; 0.884nmolCH₄m^-2s^-1, 0.715nmolCH₄m^-2s^-1, 0.694nmolCH₄m^-2s^-1 [2003-2021]. Our approach overcomes traditional model inefficiencies and resolves spatiotemporal disparities. GeoCryoAI captures abrupt and persistent changes while providing a novel methodology for assimilating contemporaneous information at various scales. We describe GeoCryoAI, the methodology, our results, and plans for future innovations and applications.