Fall detection models must generalize to diverse, uncontrolled environments for effective real-world deployment, yet achieving this remains a significant challenge in vision-based systems. This paper investigates techniques to enhance generalization, initially focusing on cross-view performance as a critical step toward broader robustness across datasets and settings. We leverage 2D pose data, synthetic training samples, and data augmentation to address this issue. A synthetic dataset was generated using Unity, simulating six 3D fall scenarios captured from ten virtual cameras with precise keypoint annotations. Experiments utilized a public, multi-view real-world dataset, training models on one camera and testing on others to simulate generalization to unseen perspectives. The baseline results showed notable drops in \(G_{mean}\) across test views, revealing overfitting to specific viewpoints. We found that lightweight data augmentations—such as flipping, rotation, and Gaussian noise—applied to pose sequences markedly improve cross-view generalization. Combining these with synthetic data further enhances performance, delivering consistent results across all cameras. The most effective strategy, integrating both approaches, achieved gains of up to 54.4% in \(G_{mean}\) over the baseline. These results emphasize the value of training diversity, via augmentation and synthetic data, in crafting robust, viewpoint-independent models. By advancing cross-view generalization, this work establishes a foundation for future efforts targeting broader generalization to diverse datasets and real-world environments, providing practical insights for assistive technology deployment.