Electricity price forecasting is crucial for effective energy management, particularly in markets characterized by high volatility. This research focuses on a comparative evaluation of Deep Learning (DL) algorithms to identify the most suitable models for predicting electricity prices. After handling missing values and normalization, we employ Principle Component Analysis to reduce the dimensionality of the dataset to make it more trainable. It leverages empirical data from Spain, encompassing electrical consumption, generation, pricing, and weather data. Six existing DL models are employed, alongside the development of two hybrid DL models stacked LSTM and CNN-LSTM aimed at improving forecasting accuracy. The results demonstrate that the Encoder-Decoder model exhibits superiority over other models, achieving an MAE of 1.6581, MSE of 4.7675, RMSE of 2.1834, and \(R^2\) of 93%. CNN-LSTM also demonstrates strong predictive capabilities, achieving similar \(R^2\) of 93% but with slightly higher error metrics. In contrast, traditional RNN and GRU models perform less effectively, with \(R^2\) values of 89% and 90%, respectively, and consistently higher error metrics. The study concludes that complex architectures like Encoder-Decoder and hybrid models such as CNN-LSTM offer superior forecasting accuracy compared to other models. This comprehensive analysis underscores the significant potential of DL models in electricity markets. Future research could explore ensemble approaches that combine multiple model architectures and incorporate additional exogenous variables to further enhance predictive performance. Furthermore, real-time implementation and scalability of these models remain pivotal for supporting dynamic electricity markets effectively.