This study examines the forecasting performance of three prominent models—Autoregressive Integrated Moving Average (ARIMA), Support Vector Regression (SVR), and Random Forest (RF)—in predicting the short-term price movements of Bitcoin (BTC) and XRP using real daily closing price data from January to June 2024. The research aims to determine which model delivers the most accurate forecasts by applying each technique to actual market data and evaluating their predictive outputs using three standard accuracy metrics: Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). Results show that ARIMA consistently outperformed both SVR and Random Forest in forecasting accuracy, recording the lowest average errors across all metrics for BTC and XRP. While SVR performed poorly in capturing XRP’s volatility, Random Forest showed moderate results but lagged behind ARIMA. These findings highlight ARIMA’s robustness and practicality as a forecasting tool for short-term cryptocurrency price movements. The study concludes with model-specific recommendations, encouraging the use of ARIMA for near-term price prediction and suggesting further exploration into hybrid and deep learning models for future research.