Sleep quality assessment is crucial for health monitoring, yet traditional polysomnography remains expensive and inaccessible for continuous monitoring. This study investigates machine learning and deep learning approaches for predicting sleep efficiency from easily measurable behavioral and physiological parameters. We evaluated three architectures—Random Forest Classifier, Long Short-Term Memory (LSTM) networks, and Feedforward Neural Networks—on a dataset of 452 individuals containing demographic, sleep pattern, and lifestyle data. The data underwent preprocessing and binary classification at a 0.55 sleep efficiency threshold, with models trained on an 80-20 train-test split. Results demonstrated that the LSTM architecture achieved superior performance with 94% test accuracy, outperforming Random Forest (92%) and Feedforward Neural Network (91%). The LSTM effectively captured complex, non-linear relationships between sleep-related features, while Random Forest provided competitive performance with greater computational efficiency. These findings validate the effectiveness of deep learning architectures for sleep quality prediction and demonstrate the feasibility of developing accessible, accurate sleep monitoring systems based on readily available measurements, potentially democratizing sleep health assessment without requiring specialized laboratory equipment.