This paper investigates the performance of a variety of machine learning models for Apple stock price prediction, covering linear regression, ridge regression, Lasso regression, SVM, weighted average, stacked model, and random forest methods. The dataset contains daily closing prices of Apple stock for the period from 2010 to 2024, using data from the first 13 years for model training and data from the last year for testing. The study results show that after many times of parameters tuning and testing, all models except Random Forest exhibit good prediction results, with simple models such as linear regression and ridge regression performing particularly well with fewer features, while the Random Forest model exhibits severe overfitting/underfitting problems. This study provides an empirical reference for the application of machine learning in financial time series forecasting, which can help to improve financial forecasting ability and investment decision-making in the future.