Geological CO2 sequestration (GCS) has been a practical approach used to mitigate global climate change. Uncertainty and sensitivity analysis of CO2 storage capacity prediction are essential aspects for large-scale CO2 sequestration. This work presents a rigorous machine learning-assisted workflow for the uncertainty and global sensitivity analysis of CO2 storage capacity prediction in deep saline aquifers. The proposed workflow comprises three main steps: 1) dataset generation — we first identify the uncertainty parameters that impact CO2 storage in deep saline aquifers and then determine their corresponding ranges and distributions. We generate the required data samples by combining the Latin Hypercube Sampling (LHS) technique with high-resolution simulations. 2) surrogate development — a data-driven surrogate is developed to map the nonlinear relationship between the input parameters and corresponding output interests from the previous step. The implementation of Bayesian optimization accelerates the tunning process of hyper-parameters instead of traditional trial-error analysis. 3) uncertainty and global sensitivity analysis — Monte Carlo simulations based on the optimized surrogate are performed to explore the time-dependent uncertainty propagation of model outputs. Then the key contributors are identified by calculating the Sobol indices based on the global sensitivity analysis. The proposed workflow is accurate and efficient and could be readily implemented in field-scale CO2 sequestration in deep saline aquifers.