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Chronic Kidney Disease (CKD) is a common and dangerous health condition that requires early detection and treatment to be effective. Current diagnostic methods are time-consuming and expensive. In this research, we hope to construct a predictive model for CKD utilizing a combination of time series and static variables for early detection of CKD. In this study, we investigate the influence of multimodal approach by combining the predictions from multiple models that utilize different modalities. The ROCKET method is utilized for classification using time series features, whilst the Random Forest approach is employed for static data. XGBoost has been utilized to gain information about feature importance among labs and demographics-comorbidities data. In this study, we use the MIMIC-III database, adopting various strategies to handle data and class imbalance, such as stratification, balancing techniques, and backwards and forward fill for missing value imputation. The evaluation metrics for CKD and non-CKD class labels include precision, recall, F1, and accuracy. Our findings show that aggregating time series data produce contrasting results for labs compared to vitals data. We also addressed the significance of the different demographic, comorbidities and lab events features. The findings indicate that a multimodal approach did not show significant advantages over individual models when the individual models performed suboptimal. The study also found that Ethnicity is more significant than age and gender in predicting CKD. Furthermore, the study revealed some significant features from lab events and comorbidities. The study also provides some recommendations for future work to explore the potential of a multimodal approach further.