Real-world time series data frequently exhibits irregular sampling intervals and may contain missing values, posing challenges for effective analysis and modeling. To handle these complexities, we present a groundbreaking approach that synergistically combines Neural Controlled Differential Equations (Neural CDEs) with Neural Flows. Central to our methodology is the introduction of a dual latent space, meticulously designed to discern and stabilize latent values amidst the irregularities intrinsic to the sampled time series data. Our empirical investigations span across 18 datasets, encompassing three distinct domains, and tested under four different missing rate scenarios. The findings consistently underscore the superiority of our proposed model over existing benchmarks in the classification of irregularly-sampled time series data. Such robust performance accentuates our model's versatility, making it a promising candidate for the real-world applications.