Predicting protein-ligand binding affinity is a fundamental challenge in computational biology and drug discovery, complicated by diverse factors including protein sequence variability, ligand chemical diversity, and structural resolution. Here, we present an integrative study that combines classical machine learning and quantum-enhanced modeling to investigate how crystal structure resolution, sequence similarity, and ligand properties jointly influence binding affinity. Using a curated \"refined\" dataset from PDBbind and an expanded general dataset, we first conduct correlation and regression analyses to quantify the relationships among binding affinity, ligand descriptors (e.g., molecular weight, logP), and protein structural metrics (resolution, R-factor). We observe moderate positive correlations between ligand size/hydrophobicity and affinity, and a slight negative correlation between resolution and affinity in the refined dataset that largely disappears in the general set. We then train multiple predictive models, including random forests, deep neural networks, and quantum-enhanced approaches--quantum kernel methods, variational quantum circuits, and a hybrid classical-quantum neural network. Experimental results show that quantum-enhanced models perform on par with classical methods in predicting binding affinities and, in some cases, offer modest improvements. Notably, a hybrid quantum-classical model achieves the highest accuracy (Pearson correlation R{approx}0.80R) on the refined dataset. These findings highlight the potential of quantum computing for capturing complex patterns in biomolecular data, laying groundwork for improved structure-based drug design. Our study underscores that while data quality and curation greatly influence observed trends, quantum machine learning despite current hardware limitations can already serve as a competitive and promising tool in computational structural biology.