A timely diagnosis and treatment of sleep disorders in neonates during their first week of life is crucial. Current methods for staging neonatal sleep rely heavily on multiple electroencephalography (EEG) channels. These channels increase computational complexity, require a large amount of data to be transferred to the cloud, and may cause skin irritation. We propose an innovative automated classification approach that integrates multi-view feature fusion, AdaptiSelect-based feature optimization, the smart cloud data transfer and reconstruction (STREAM) module, and a rotational ensemble stacking model. The data reduction module significantly enhances edge-cloud systems’ performance in IoT-based healthcare environments by reducing data transmission by a factor of 153.6 through efficient feature selection and compact data packet formation. This module ensures minimal bandwidth usage, reduces the computational load on resource-constrained edge devices, and lowers cloud storage requirements while maintaining full data reconstruction. The dataset used in this research combines two large datasets collected over four years from the Children’s Hospital Fudan University, Shanghai. A unique set of 315 features are extracted from each epoch of a single channel using flexible analytical wavelet transform (FAWT), dual-tree complex wavelet transform (DTCWT), enhanced covariance (ECOV), and spectral features based on α, β, θ, and δ brain waves. These features are refined using AdaptiSelect, achieving an accuracy of 81.16% and a Kappa of 72.17% with one channel. Accuracy improves to 82.79% with a Kappa of 74.70% when using two channels, validated through 10-fold cross-validation. Additionally, Leave-One-Subject-Out crossvalidation (LOSO-CV) further demonstrates the effectiveness of the proposed approach as a generalized solution. Using both single and multichannel setups, the proposed approach outperforms the most significant state-of-the-art methods in neonatal sleep analysis.