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Predicting periods of heightened stock-price volatility helps investors and policy makers manage risk during geopolitical and macroeconomic shocks. This study models the short-term volatility of seven influential U.S. technology companies—Apple, Microsoft, Alphabet, Amazon, Nvidia, Tesla and Meta—collectively known as the “Magnificent Seven.” We build classification models to distinguish between high- and low-volatility regimes using daily stock prices, technical indicators and sentiment signals derived from tariff news between 1 January 2018 and 30 April 2025. The United States Trade Representative announced in May 2024 that tariffs on semiconductors will rise from 25% to 50% and tariffs on electric vehicles will increase from 25% to 100% these actions highlight the importance of trade policy for tech stocks. Our methodology computes a rolling 14-day standard deviation to label volatility regimes and applies logistic regression, decision trees and random forest classifiers. The random forest model tuned with Optuna outperforms other methods, achieving 0.69 accuracy, 0.64 precision, 0.65 recall, 0.64 F1 and a ROC–AUC of 0.72 on out-of-sample data. Feature importance analysis shows that tariff sentiment, average true range and Bollinger band width are the strongest predictors of volatility. The models and visualizations, along with a reproducible code appendix, offer investors and policy makers a transparent framework for assessing the impact of tariff announcements on market turbulence.