Big data technologies, such as machine learning, have increased data utility exponentially. At the same time, the cloud has made the deployment of these technologies more accessible. However, computations of unencrypted sensitive data in a cloud environment may expose threats and Cybersecurity attacks. We consider a class of innovative cryptographic techniques called Privacy-Preserving Technologies (PPTs) to address this problem. That might help increase utility by taking more significant advantage of the cloud and machine learning technologies while preserving privacy. The first section provides a brief introduction to the so-called Homomorphic Encryption "HE" by giving an overview of the most promising schemes and then giving the current state of the art of HE tools such as Libraries and Compilers. This section aims to help non-cryptographer developers propose HE solutions by explaining what makes developing HE applications challenging. Then, we address the Privacy-Preserving in Machine Learning (PPML), an approach that allows to train and deploy ML models without exposing their private data. After exploring state-of-the-art for the most used ML models in PPML, we will overview applications of Homomorphic Encryption in Machine Learning.