###### Abstract  
The accurate prediction of cryptocurrency prices is crucial due to the volatility and complexity of digital asset markets, which pose significant challenges to traders, investors, and researchers. This research addresses these challenges by leveraging machine learning and deep learning techniques to forecast closing prices for cryptocurrencies, focusing on Bitcoin, Ethereum, Binance Coin, and Litecoin cryptocurrency datasets. A Random Forest ensemble learning algorithm, a Gradient Boosting model, and a feedforward neural network were implemented to handle the complexities in cryptocurrency data. A Z-Score-based anomaly detection framework was integrated to classify closing prices as normal or abnormal, aiding in identifying significant market events. Evaluation metrics, such as the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R\({}^{2}\)), demonstrate the superior precision and reliability of the Random Forest and Gradient Boosting models. The deep learning model indicates strong generalization capabilities, suggesting potential advantages on more complex datasets. These findings highlight the importance of combining advanced machine learning techniques and cryptocurrencies to develop a robust framework for cryptocurrency forecasting and anomaly detection.  
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understanding the complex challenges of predicting the cryptocurrency market, providing a more advanced analytical tool for investors and financial analysts.  
This study contributes to the existing knowledge base by integrating machine learning and deep learning to forecast cryptocurrencies' closing prices and identify irregular market trends. This study used historical price data and essential market features such as trading volume and capitalization to develop robust predictive models. By leveraging Random Forest and Gradient Boosting algorithms, along with a feedforward neural network, this research addressed the non-linear patterns and dynamic nature of cryptocurrency data. Furthermore, an anomaly detection framework based on Z-Scores was incorporated to classify closing prices as normal or abnormal, allowing the identification of significant market events that could impact trading strategies.  
A fundamental aspect of this research is enhancing cryptocurrencies by enabling early and transparent decisions based on accurate prediction, which helps to prevent or minimize harmful situations. This approach ensures the clarity, immutability, and real-time accessibility of abnormal trading data, thereby enhancing the reliability and utility of the predictive framework. This investigation provides a comprehensive solution for cryptocurrency price forecasting, anomaly detection, and market analysis. By addressing existing limitations in traditional financial forecasting methods, this research contributes to advancing the field of cryptocurrency analytics and offers practical insights for stakeholders in the digital asset ecosystem.  
The key contributions of this research are summarized as follows:  
* Integration of Advanced Technologies: A robust framework combines machine learning, deep learning, and cryptocurrency to improve price prediction and anomaly detection while ensuring transparency and data immutability.
* Robust Predictive Models: This research utilizes Random Forest, Gradient Boosting, and feedforward neural network models to tackle non-linear behaviors and evolving patterns in cryptocurrency data. It incorporates features such as trading volume and market capitalization to improve the accuracy of the models.
* Anomaly Detection Framework: A Z-score-based mechanism classifies closing prices as normal or abnormal, providing insights into significant market events to support trading strategies and risk management.
* Advancement of Cryptocurrency Analytics: This research offers practical tools for stakeholders to tackle the challenges of the cryptocurrency market by overcoming the limitations of traditional forecasting methods.