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Mild cognitive impairment (MCI) and dementia significantly impact millions worldwide and rank as a major cause of mortality. Since traditional diagnostic methods are often costly and result in delayed diagnoses, many efforts have been made to propose automatic detection approaches. However, most methods focus on monolingual cases, limiting the scalability of their models to individuals speaking different languages. To understand the common characteristics of people with MCI speaking different languages, we propose a multilingual MCI detection model using multimodal approaches that analyze both acoustic and linguistic features. It outperforms existing machine learning models by identifying universal MCI indicators across languages. Particularly, we find that speech duration and pauses are crucial in detecting MCI in multilingual settings. Our findings can potentially facilitate early intervention in cognitive decline across diverse linguistic backgrounds.