This study presents a comparative evaluation of 11 predictive models for forecasting Bitcoin (BTC) prices. The models cover a wide range of techniques, including statistical methods (ARIMA, GARCH, SARIMA), machine learning algorithms (SVR, Random Forest, XGBoost, LightGBM), deep learning architectures (LSTM, GRU, Transformer, WaveNet), and reinforcement learning approaches. Daily BTC-USD data from January 2018 to April 2024 were used to train and evaluate models. In addition, two sentiment analysis tools (VADER and FinBERT) were incorporated to examine the impact of public sentiment on price behavior. Model performance was assessed using four standard metrics: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), complemented by visual comparisons. Results show that machine learning and deep learning models, particularly LightGBM, WaveNet, and SVR, consistently outperform traditional statistical methods in terms of accuracy and adaptability. Moreover, the inclusion of sentiment-based features proved valuable during periods of high market volatility. Overall, the findings support the effectiveness of multidimensional modeling strategies that combine quantitative precision with sentiment-informed insights, especially in the context of the highly dynamic cryptocurrency market.