Cell load estimation accuracy can be enhanced through the utilization of advanced machine learning techniques such as deep learning algorithms. Deep learning models, particularly recurrent neural networks (RNNs) and long short-term memory (LSTM) networks, can be employed to capture the temporal dependencies in cell traffic data. By training these models on historical cell load data, they can learn complex patterns and relationships, enabling more accurate predictions of future cell loads. Additionally, ensemble learning methods like random forests or gradient boosting can be used to combine multiple models and improve estimation accuracy further. These techniques can handle non-linear relationships and fluctuations in cell traffic loads more effectively, leading to enhanced estimation performance.