Conventional metrics for evaluating equality in urban green space planning, primarily based on two-dimensional green space (2DGS) area coverage, are insufficient for characterizing the intricate distribution of green resources in three-dimensional space. These traditional measures overlook the substantial contributions of vertical vegetation structures, such as trees and shrubs, to urban ecological health, biodiversity, and aesthetic quality, potentially leading to suboptimal planning outcomes. To address this gap, this study establishes a novel analytical framework that synergizes high-precision remote sensing with advanced machine learning to enable a precise and comprehensive assessment of equality in three-dimensional green volume (3DGV) allocation, thereby supporting more integrated and effective urban green planning. The framework was implemented in the main city of Nanjing through four sequential stages: 1) 2DGS Extraction: Precise delineation of surface green coverage was achieved by applying a Support Vector Machine (SVM) classifier to high-resolution remote sensing imagery, generating an accurate foundational map for spatial analysis. 2) 3DGV Modeling and Calculation: A robust Random Forest regression model was developed to estimate 3DGV across the entire study area. This model was trained using detailed field-measured 3DGV data from sample plots and a suite of spectral vegetation indices derived from satellite data, effectively capturing non-linear relationships to ensure reliable volume inversion. 3) Accessibility-Based Equity Measurement: The equity of access to green resources was evaluated using the Two-Step Floating Catchment Area (2SFCA) method. This approach calculated the accessible 3DGV for each residential community by balancing the spatial supply of green volume with local population demand, highlighting fine-grained spatial disparities in availability. 4) Comparative Equity Analysis: The overall equity level was quantified using the Gini coefficient for accessible 3DGV, while the location quotient identified units with relative surplus or deficit. A direct comparison of equity indicators derived from 3DGV and conventional 2DGS metrics revealed the distinct analytical insights provided by the volumetric perspective. The analysis yields several key findings. First, inequalities in 2DGS distribution are amplified in the 3DGV, indicating that area-based disparities lead to even greater volumetric imbalances. Second, the dynamics of 3DGV are more complex and sensitive; it exhibits a lagged and non-linear response to significant changes in surface greenery, making its equity patterns during urbanization more volatile than those of 2DGS. This underscores the critical limitation of relying solely on planar metrics, as they mask these heightened and more sensitive volumetric inequalities. Therefore, the study concludes that achieving equitable green space allocation requires planning policies and regulatory interventions specifically designed to monitor and manage 3DGV. The proposed framework offers a multidimensional, scientifically robust basis for spatial decision-making, providing innovative methodology for advancing sustainable and just urban green environments.