In recent years, machine and deep learning models have attracted significant attention for electricity price forecast in global wholesale electricity markets. Yet, a predominant focus on point forecast in most parts of literature limits the practical application of these models due to the absence of uncertainty quantification. In this study, we first perform an analysis of the electricity price trends in the Mexican wholesale electricity market to determine the influence of key variables. Using independent component analysis and wavelet coherence analysis, we were able to identify primary determinants influencing locational marginal electricity prices. Subsequently, we applied four different models covering the most important algorithms proposed in the literature for electricity price forecast. Our findings revealed that the most accurate forecasting results were achieved using a deep learning‐based method with a decision tree‐based model trailing closely. Finally, we incorporate conformal prediction for uncertainty quantification by calculating the prediction intervals with a target coverage level of 95%. The conformal prediction intervals provide a more comprehensive view of the possible future scenarios, enhancing economic efficiency, risk management, and decision‐making processes. This is particularly important because of the dynamic nature of electricity markets, where prices are strongly influenced by multiple factors. This study demonstrates the potential of using hybrid models for electricity price forecasting The findings suggest that natural gas prices would be sufficient for electricity price forecast in the Mexican market resulting in a simple model for deployment thus significantly reducing the computational costs. LSTM‐based and decision tree‐based models outperformed the ‘classical’ models in terms of accuracy and uncertaninty. This research offers significant insights for policymakers, energy traders, the academic community, and other key stakeholders, enhancing their understanding of the impact of exogenous variables on electricity prices. Finally, we incorporate conformal prediction for uncertainty quantification by calculating the predication intervals with a target coverage level of 95%.