Ensuring the stability and safety of humanoid robots during dynamic tasks remains a crucial challenge. The ability to predict and prevent falls is pivotal in such contexts, as falls can hinder performance and waste resources. Existing fall detection and prediction methods for humanoid robots vary in their approaches, and data-driven approaches based on neural networks have shown promising results. Nevertheless, these approaches still lack the efficiency required to provide fall predictions within an acceptable timeframe without compromising accuracy. In this paper, we propose a data-driven approach leveraging a lightweight neural network architecture, enabling accurate fall predictions in near real-time. Our framework, relying on raw IMU sensor data, undergoes comprehensive evaluation across diverse fall scenarios and evaluation metrics. Comparative analyses against baseline architectures from the literature affirm its superior performance in humanoid fall detection and prediction.