Cryptocurrency markets have gained significant attention due to their rapid growth, high price volatility, decentralized nature, and increasing participation by individual and institutional investors. However, predicting cryptocurrency prices remains a major challenge because of unpredictable market movements, changing investor sentiment, economic developments, and technological advancements. Traditional forecasting methods, such as Moving Average (MA), Autoregressive Integrated Moving Average (ARIMA), and Exponential Smoothing, have been widely used to analyze historical price patterns and forecast future market trends. In recent years, Artificial Intelligence (AI)-based techniques, including Machine Learning (ML), Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM) networks, and Random Forest algorithms, have emerged as alternative approaches for cryptocurrency price prediction. These methods can identify complex relationships, nonlinear patterns, and trends within large datasets. This research aims to comparatively evaluate AI-based and traditional forecasting methods in terms of prediction accuracy, reliability, adaptability, computational efficiency, and performance under volatile market conditions. The study focuses on cryptocurrency price data, particularly Bitcoin and Ethereum, to examine the effectiveness of different forecasting techniques using quantitative performance measures such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The findings are expected to provide insights into the relative strengths and limitations of both approaches and identify suitable forecasting techniques for cryptocurrency market analysis. The study may contribute to researchers, investors, financial analysts, and technology professionals seeking to understand the role of AI in improving cryptocurrency price forecasting. However, accurate price prediction does not guarantee profitable investment decisions because cryptocurrency markets remain highly uncertain and susceptible to sudden changes.