Personality prediction helps us understand human behavior, decision-making, and social interactions. One of the most well-known frameworks for assessing personality is the OCEAN model. The concept of using machine learning (ML) to predict personality traits is based on this model. The idea is that personality traits can change over time, much like ocean waves, and by analyzing these patterns using ML algorithms, we can make accurate predictions about an individual’s personality. Personality prediction using machine learning plays an important role in fields such as human resources, marketing, and mental health. In this paper, we present the implementation of OCEAN personality prediction using unsupervised machine learning. The goal is to develop a platform that can predict personality traits and explore how they relate to each other. To achieve this, we introduced an approach for predicting OCEAN personality traits using two clustering techniques: K-Nearest Neighbors (KNN) and Gaussian Mixture Models (GMM). We evaluated our model using the Big Five Personality Test dataset from Kaggle and measured its performance with three widely used clustering metrics: the Davies-Bouldin Index, Calinski-Harabasz Score, and Silhouette Coefficient. Specifically, when comparing KNN to GMM, the Davies-Bouldin Index was significantly lower (2.3867 vs. 7.7511), the Calinski-Harabasz Score was substantially higher (74,967.3736 vs. 12,732.4629), and the Silhouette Coefficient was also improved (0.0760 vs. −0.0240), demonstrating the effectiveness and accuracy of our proposed approach. Overall, this method moves away from traditional supervised models and offers a more flexible, cost-effective way to understand personality traits in ever-changing digital environments. By comparing the performance of these unsupervised models with traditional ones, this research aims to push forward the development of practical, general-purpose personality prediction tools.