Background: Radiopharmaceutical drug conjugates (RDCs) represent a transformative paradigm in precision oncology, yet target-specific ligand discovery remains a critical bottleneck. We developed an integrated AI-augmented platform to rapidly identify cyclic peptide binders for two emerging RDC targets: B7-H3 (immune checkpoint) and DLL3 (Notch ligand). Methods: A structurally diverse phage display library (1.5×10 11 capacity; 8-17 aa macrocycles) was engineered with NGS-validated complexity. Recombinant 4Ig-B7-H3 (2Ig-B7-H3 as off-target counterscreens), DLL3 (and counterscreens DLL1/DLL4) underwent orthogonal biophysical characterization by SPR and Spectral shift assay (SPS). Hit triangulation employed: (1) Deep sequencing-driven consensus motif analysis, (2) AlphaFold3 multimer modeling of peptide-target complexes, and (3) Parallel SPR/spectral shift assays (10⁻⁷ M affinity threshold). Top candidates were Cy5-labeled for real-time binding and internalization kinetics in engineered tumor lines. Results: Discovery: 15/40 phage clones demonstrated target binding - AI optimization: AlphaFold3 predictions revealed a conserved beta-turn motif in 7/15 top hits that anchors to a cryptic pocket in targets - Validation: Hit peptide were synthesized and showed: (i) KD 8.2×10 ⁻7 M (SPR), (ii) >3-fold selectivity over homologous target proteins (iii) showed binding on cells Conclusions: This platform addresses critical challenges in RDC development by providing a streamlined solution for hit discovery, optimization, and validation. The integration of AI-driven structural prediction with high-throughput experimental validation compresses traditional hit discovery timelines This platform will significantly accelerate the RDC development pipeline-especially for hit binder identification for neo tumor antigens or other validated surface proteins like GPCRs, transporters, to accelerate RDC drug discovery using cyclic peptide as the modality.