Selecting the best smartphone can be challenging due to the wide range of models available on the market. This study shows how the machine learning models can predict mobile phone prices based on their features We evaluated several machine learning techniques, including Logistic Regression, Decision Trees, Random Forest, SVC, K-Neighbors Classifier, Gaussian Naive Bayes (GaussianNB), AdaBoost, Gradient Boosting, Extra Trees, Bagging Classifiers, and XGBoost. The primary objective was to identify the most effective model for price forecasting and to investigate the factors influencing phone prices. Our research offers insights to both consumers and manufacturers, helping them make more informed decisions about phone features and pricing. We emphasize the importance of using diverse datasets that accurately represent various smartphone models and pricing points. Key factors affecting phone costs were identified, and model performance was assessed using metrics such as accuracy, F1-score, and classification reports. Model performance was further enhanced through hyperparameter tuning with GridSearchCV, achieving 97% accuracy with the Decision Tree, K-Neighbors Classifier, SVC, AdaBoost, and Random Forest models. Among these, the Decision Tree and SVC was selected as the optimal models, offering a good tradeoff between accuracy, flexibility, and time complexity. This study aims to provide valuable data to guide consumers in making informed choices about mobile phone features and price ranges.