Macrocyclic drugs offer powerful opportunities for modulating protein-protein interactions, yet their development is limited by poor and unpredictable membrane permeability. Experimental testing is slow, and 3D modeling of macrocycles is computationally demanding due to their large conformational space. To address this, we present Multi_DDPP, a deep learning (DL) model that predicts macrocycle permeability directly from 2D structures. Multi_DDPP employs knowledge distillation to leverage permeability data from multiple cell lines, improving generalizability, and uses a task-specific swing-range strategy to reduce label noise. By integrating diverse molecular representations, including physicochemical descriptors, fingerprints, molecular graphs, and hybrid features, the model outperforms existing ML and DL approaches. Node masking highlights the substructures that contribute most to permeability, and regression extensions incorporating physiological parameters further refine these predictions. Early 2D-based permeability prediction with Multi_DDPP avoids the costly generation of 3D conformers and enables the efficient prioritization of macrocycles with favorable pharmacokinetic potential.