With the advancement of unconventional reservoir development, the gas channeling challenge in CO2 flooding processes has been identified as a critical issue, particularly in gravity-dominated displacement scenarios where accurate prediction and control are significantly complicated. The limitation of conventional gas flooding characteristic curves that neglect oil-gas gravity segregation mechanisms is systematically addressed through the development of a multi-field constrained gravity correction model. Key geological parameters including reservoir dip angle (θ), vertical permeability variation coefficient (Vk), and fluid density differential (Δρ) are computationally integrated, with concurrent refinement applied to the dimensionless gravity stability index ( ) for enhanced characterization of gravity-driven channeling effects.Three methodological innovations are presented through rigorously structured implementation phases. Initially, a dip-angle-incorporated seepage model is formulated through derivation of revised material balance equations, yielding a novel inverse S-curve formulation. Subsequently, four critical parameters (Vk, δ, ag, θ) are extracted and correlated with historical production data, enabling quantitative classification of gravity-mediated channeling patterns into four distinct categories: gravitational stabilization, weak channeling, gravitational failure, and complex transitional types, each with corresponding mitigation strategies. Ultimately, a machine learning framework is established through integration of segmented inverse S-curve constraints with gravity parameters and multimodal reservoir data, implementing an LSTM-GARCH hybrid model for intelligent channeling early-warning in structurally inclined reservoirs.Field validation in the X54 well block demonstrated operational superiority, achieving 7-day predictive capability with 40% accuracy improvement over conventional methods. A complete technical system encompassing theoretical derivation, intelligent algorithm development, and field implementation has been successfully established, providing a replicable template for gas injection optimization in analogous heterogeneous reservoirs.