In data analysis, data imputation is an essential procedure, especially when working with partial datasets. Machine learning models' validity and performance can be significantly impacted by missing data. Conventional techniques for data imputation, including regression models or mean/mode imputation, frequently fall short of capturing the complex relationships present in the data. In order to increase the precision and resilience of data imputation, this research suggests a hybrid methodology that integrates several AI paradigms, such as machine learning, deep learning, and statistical techniques. The suggested hybrid strategy performs better than traditional methods in a variety of contexts, according to experimental results, providing a more dependable way to handle missing data in complicated datasets.