As Code Language Models (CLMs) are increasingly used to automate multilingual code intelligence tasks, Full-Parameter Fine-Tuning (FPFT) of CLMs has become a widely adopted approach, which is both time-consuming and resource-intensive. Parameter-Efficient Fine-Tuning (PEFT) provides a more efficient alternative to FPFT. However, it struggles to capture common features shared across languages, leading to performance degradation. Recent studies have explored mixed-language training with PEFT to avoid the loss of common features. However, these methods can result in gradient conflicts due to the diverse language-specific features, causing suboptimal performance, particularly for low-resource languages. In this paper, we propose Mixture-of-Experts Multilingual Low-Rank Adaptation (MMLoRA) for multilingual code summarization. MMLoRA addresses gradient conflicts while preserving common features shared across languages by combining a universal expert with a set of specialized linguistic experts. Additionally, we introduce an expert loss function that maintains the diversity of specialized linguistic experts while balancing the learning progress. Experimental results indicate that MMLoRA achieves state-of-the-art performance in multilingual code summarization while maintaining efficient fine-tuning. The performance improvement is particularly significant in low-resource languages such as Ruby.