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 (V_k), and fluid density differential (Δρ) were computationally integrated, with concurrent refinement applied to the dimensionless gravity stability index (${G}_{\mathrm{s}}^{\theta }$) for enhanced characterization of gravity-driven channeling effects.Three methodological innovations were presented through rigorously structured implementation phases. Initially, a dip-angle-incorporated seepage model was formulated through derivation of revised material balance equations, yielding a novel inverse S-curve formulation. Subsequently, four critical parameters [V_k, δ, air drive acceleration(a_g), θ] were 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 was established through integration of segmented inverse S-curve constraints with gravity parameters and multimodal reservoir data, implementing an (long short-term memory-generalized autoregressive conditional heteroskedasticity,LSTM-GARCH) hybrid model for intelligent channeling early-warning in structurally inclined reservoirs.Field validation in the X54 well block demonstrate 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 was successfully established, providing a replicable template for gas injection optimization in analogous heterogeneous reservoirs.