Fault detection in power distribution systems is essential not only for maintaining system reliability, but also for ensuring security against potential disruptions. Traditional methods, which typically analyze fault waveform recordings, often struggle due to the limited availability of such recordings and the varying nature of faults. In this paper, we introduce an innovative meta-learning-based fault detection framework that combines the model-agnostic meta-learning (MAML) with a long short-term memory (LSTM) network, Multi-Head Attention mechanisms, and Adversarial Learning. This approach is uniquely designed to enhance model adaptability and robustness in low-data, high-variability fault scenarios, addressing significant limitations of conventional methods. The LSTM network is designed to handle time-series data, making it well-suited for capturing key patterns in fault waveforms. By integrating multi-head attention, the model focuses on critical time intervals, ensuring that the most relevant features are extracted. Adversarial learning enhances model robustness, improving generalization in noisy and adversarial conditions. Our approach is tested on multiple fault scenarios, demonstrating significant improvements in fault detection accuracy, especially in low-data and high-variability conditions. This method not only improves detection accuracy but also offers increased stability under adversarial conditions, making it a promising solution for power distribution fault detection.