This paper investigates the use of Fully Homomorphic Encryption (FHE) through the ConcreteML library to ensure data privacy in machine learning applications. The research focuses on predicting genetic disorders using a Gradient Boosted Tree model trained on a dataset of 22,000 genomic samples. ConcreteML enables encrypted inference without exposing sensitive patient data during cloud-based processing. The results show that FHE-enabled models maintain high accuracy, comparable to their plaintext counterparts, with acceptable performance trade-offs. The thesis also discusses key technical challenges, including quantization, model compilation, and managing computational overhead. Ultimately, this work demonstrates the viability of secure, privacy-preserving machine learning in genomics using modern FHE tools.