###### Abstract
Consequently, accurate SMP forecasting in Turkey requires models that are tailored to these specific conditions, incorporating a diverse set of variables to capture the multifaceted nature of price fluctuations [9]. This study introduces a comprehensive forecasting framework that integrates a range of statistical, machine learning, and deep learning models, alongside advanced feature selection techniques. The primary objective is to enhance SMP prediction accuracy and reliability, providing market participants with critical insights for informed decision-making. To achieve this, the study incorporates feature selection algorithms like Minimum Redundancy Maximum Relevance (mRMR) and Maximum Likelihood Feature Selector (MLFS), which filter out redundant or less informative variables, thereby refining input features to enhance model interpretability and predictive performance. The modeling framework includes traditional statistical methods, such as the Autoregressive Integrated Moving Average (ARIMA) and its Seasonal variant (SARIMA), serving as baseline models for capturing linear trends and seasonal patterns within SMP data. However, to handle non-linear dependencies and high-dimensional interactions often present in electricity price data, the study incorporates machine learning models, including Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Extreme Learning Machine (ELM). Each of these models contributes unique strengths for handling complex non-linear data patterns. In addition to machine learning techniques, the framework leverages advanced deep learning models to capture temporal dependencies within SMP data more effectively. Long Short-Term Memory (LSTM) networks are employed for their capability to handle sequential data and address challenges like the vanishing gradient problem, while Bidirectional LSTM (Bi-LSTM) provides a bidirectional perspective on temporal data, capturing both past and future dependencies. Convolutional LSTM (ConvLSTM) is also incorporated to model spatial and temporal dependencies jointly, enhancing the model's capability to understand complex patterns over time. To further improve model robustness and adaptability, tree-based ensemble methods like eXtreme Gradient Boosting (XGBoost) are used. XGBoost efficiently manages non-linear interactions and mitigates overfitting through iterative boosting and regularization techniques. Each model in the framework is fine-tuned with hyperparameter optimization, leveraging grid search and cross-validation to achieve optimal predictive performance. This integrated methodology, combining diverse forecasting models with advanced feature selection techniques (mRMR and MLFS), enablesa flexible and robust forecasting framework tailored to address the specific dynamics of SMP in the Turkish electricity market. The findings of this study make several key contributions to the field of SMP forecasting. First, the integration of machine learning and statistical techniques, coupled with rigorous feature selection, enables the development of hybrid models that effectively capture the non-linear and dynamic nature of SMP. Second, the use of advanced feature selection algorithms significantly enhances model performance by reducing dimensionality and focusing on the most relevant predictors, thereby improving interpretability and predictive accuracy. Third, the application of these models to the Turkish electricity market addresses a critical gap in the literature, as previous research has largely focused on other regions with different market structures and regulatory environments. The results demonstrate that both the ELM and XGBoost models yield high prediction accuracy, underscoring their potential for real-world SMP forecasting applications. The rest of this study is structured as follows: Section 2 provides an overview of the existing literature on SMP forecasting, identifying key gaps and areas for further research.