Virtual currencies have become more prevalent in financial transactions, and Bitcoin (BTC) is regarded as the most important cryptocurrency in the world because of its high market capitalization and higher technological infrastructure. Many researchers have used Machine Learning (ML) approaches as an effective tool to study the factors affecting the BTC price and the patterns behind its fluctuations. In this paper, the existing literature to identify challenges and eliminate errors in the current BTC price prediction models are reviewed. In addition, a new Bitcoin Price Prediction (BTCPP) Framework is proposed for predicting BTC prices based on Deep Learning (DL) approach. The proposed framework uses the DL regression and classification technique, specifically regression and classification methods, to predict future cryptocurrency prices. The development of this framework involves a five-step process: feature selection, training, forecasting, optimization, and validation. To assess the reliability and robustness of the proposed framework, a comparative analysis is performed against the other existing models. Additionally, the BTCPP framework is designed to be adaptable and can be extended to other cryptocurrencies using advanced DL algorithms. This adaptability provides a strategic advantage for price prediction in the volatile cryptocurrency market.