The increasing adoption of electric vehicles (EVs) necessitates accurate prediction of charging loads to manage grid stability and integrate renewable energy sources effectively. This study evaluates the performance of various deep learning models, including Convolutional Neural Network Long Short-Term Memory (CNN-LSTM), Simple Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Stacked LSTM, Bidirectional LSTM (Bi-LSTM), and Simple Artificial Neural Network (ANN), for predicting EV charging loads. Using the real-world datasets, charging events are analyzed to illustrate charging patterns and assess the applicability and performance of deep learning techniques. The methodology involves data preprocessing, model development, hyperparameter optimization, and performance evaluation using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) metrics, and visual inspection. The results demonstrate that hybrid models, such as CNN-LSTM and Bi-LSTM, outperform traditional methods, capturing complex temporal patterns and dependencies more effectively.