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This study examines the suitability of language-agnostic features for automatically detecting Mild Cognitive Impairment (MCI) and predicting Mini-Mental State Examination (MMSE) scores in a multilingual framework. We explored two methods for feature extraction: traditional feature engineering and pre-trained feature representation. We developed our models using the Interspeech 2024 Taukadial challenge data set, containing audios from subjects with MCI and controls in Chinese and English. Our top ensemble model achieved 75% accuracy in MCI detection and an RMSE of 2.44 in MMSE prediction in the testing set. Our results reveal the complementary nature of acoustic and linguistic representations and the existence of universal features that can be used cross-lingually. However, a statistical analysis of interpretable features did not show any shared speech patterns between the two languages, which can be attributed to differences in disease severity between the two cohorts of participants.