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Abstract: Cryptocurrency has emerged as a transformative force in the financial realm, garnering widespread attention and acceptance. However, its dynamic nature and inherent uncertainties pose significant challenges for investors. This study delves into the factors shaping cryptocurrency value formation by harnessing the power of advanced artificial intelligence frameworks. Specifically, we employ fully connected Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) Recurrent Neural Network to analyze the price dynamics of prominent cryptocurrencies such as Bitcoin, Ethereum, and Ripple. Our research reveals that ANN tends to rely more heavily on long- term historical data, whereas LSTM exhibits a penchant for short-term dynamics. Interestingly, LSTM demonstrates superior efficiency in leveraging historical information, yet with adequate data, ANN can achieve comparable accuracy. Our findings shed light on the predictability of cryptocurrency market prices, albeit the interpretation may vary depending on the machine-learning model utilized. This study underscores the significance of leveraging artificial intelligence in comprehending and forecasting cryptocurrency market trends, thereby mitigating investment risks in this dynamic landscape.