<p>Personality traits play a pivotal role in shaping human behavior and decision-making, and their accurate prediction has garnered significant research interest. Traditionally, personality prediction has relied on self-reported questionnaires; however, advancements in technology have enabled alternative, indirect methods. Users’ interactions within digital environments generate behavioral footprints that can be leveraged for applications in psychology and human-computer interaction. Smartphones, as ubiquitous tools, provide rich data that reveal behavioral patterns, some of which are predictive of personality traits. This paper utilized smartphone data, specifically Call Detail Records (CDR) and Mobile Internet Usage (MIU) logs, to predict Big Five (OCEAN) personality traits. Data collection was facilitated through an Android application installed by 67 voluntary participants, who also completed an online personality questionnaire. Over an average of 30 days, behavioral features were extracted and used in linear regression and Gaussian process models for prediction. To address limited sample size, data augmentation techniques were employed to generate synthetic data, allowing further evaluation using diverse machine learning methods. This study highlights several strengths, including the extraction of novel features, the combined use of MIU and CDR, the higher granularity of collected logs, and diverse predictive methods. The results indicate that all OCEAN traits were predicted with acceptable accuracy, with MIU features outperforming CDR. Gaussian process methods demonstrated superior performance compared to linear regression. On augmented dataset, machine learning methods achieved remarkable accuracy (RMSE ~ 0.1). These findings validate the proposed framework, offering a robust approach for personality prediction and implications for interdisciplinary applications in psychology and telecommunication.</p>