The Zika virus, a mosquito-borne disease primarily transmitted by Aedes aegypti, poses a serious public health risk, particularly in tropical and subtropical regions such as India. Factors like climate change, rapid urbanization, and improper water management contribute significantly to its spread. This study explores how various environmental and human factors influence the transmission of the Zika virus and examines the role of preventive measures such as public awareness campaigns and improved hygiene practices. Additionally, this research integrates machine learning techniques with secure data management methods to predict Zika virus outbreaks more accurately. By applying Multilayer Perceptron (MLP) classifiers and hybrid encryption techniques, the study ensures both reliable disease forecasting and enhanced data security. The findings highlight the potential of AI-driven models in epidemic control while emphasizing the need for robust data protection strategies.