Background and Objective: Neonatal health is critical for early infant care, where accurate and timely diagnoses are essential for effective intervention. Traditional methods such as physical exams and laboratory tests may lack the precision required for early detection. Hyperspectral imaging (HSI) provides non-invasive, detailed analysis across multiple wavelengths, making it a promising tool for neonatal diagnostics. This study introduces HarmonyNet, an involution-based HSI model designed to improve the accuracy and efficiency of classifying neonatal health conditions. Methods: Data from 220 neonates were collected at the Neonatal Intensive Care Unit of Selçuk University, comprising 110 healthy infants and 110 diagnosed with conditions such as respiratory distress syndrome (RDS), pneumothorax (PTX), and coarctation of the aorta (AORT). The HarmonyNet model incorporates involution kernels and residual blocks to enhance feature extraction. The model's performance was evaluated using metrics such as overall accuracy, precision, recall, and area under the curve (AUC). Ablation studies were conducted to optimize hyperparameters and network architecture. Results: HarmonyNet achieved an AUC of 98.99%, with overall accuracy, precision and recall rates of 90.91%, outperforming existing convolution-based models. Its low parameter count and computational efficiency proved particularly advantageous in low-data scenarios. Ablation studies further demonstrated the importance of involution layers and residual blocks in improving classification accuracy. Conclusions: HarmonyNet represents a significant advancement in neonatal diagnostics, offering high accuracy with computational efficiency. Its non-invasive nature can contribute to improved health outcomes and more efficient medical interventions. Future research should focus on expanding the dataset and exploring the model's potential in multi-class classification tasks.