This research examines ways to enhance intervention strategies for the elderly's functional movement by optimizing machine learning algorithms like support vector machine (SVM), random forest (Random Forest), and convolutional neural network (CNN). By incorporating techniques like choosing features, fine-tuning hyperparameters, and combining models, we greatly enhanced the accuracy, precision, recall, and F1-score of the model. Practical application results show that the optimized model can efficiently identify various actions of the elderly, especially the CNN model, which performs best on all evaluation indicators. Furthermore, we extensively talked about implementing interventions like functional movement screening (FMS), aerobic, resistance, balance, and flexibility exercises, in line with the World Health Organization (WHO) recommendations. These actions greatly enhance seniors’ physical abilities, decrease the chance of falls, and enhance overall well-being, offering a scientific foundation for achieving optimal aging. In conclusion, this research offers a solid theoretical foundation and useful advice for implementing functional movement strategies for older individuals, aiding in the promotion of healthy aging.