The global population is rapidly aging, which poses significant challenges for healthcare systems worldwide, including increased costs and a rising demand for effective geriatric care. Addressing these challenges necessitates innovative approaches to improve the early detection and prediction of frailty among elderly individuals, aiming to alleviate healthcare burdens and enhance quality of life. This study focuses on the development of a machine learning system aimed at improving early detection and prediction of frailty among elderly populations. To achieve these results we used the FRELSA dataset, a frailty-specific dataset originated ELSA, an influential longitudinal study on aging with 9 waves of data collection and more than 5000 participants. The research begins by optimizing clinical data collection through feature extraction to enhance efficiency in frailty assessment. Various machine learning techniques, including Multilayer Perceptron (MLPs) and Convolutional Neural Networks (CNNs), are evaluated for their ability to predict frailty basedon the identified features. Additionally, the study explores temporal dependencies within data to gain insights into the progression of frailty and to facilitate more personalized patient care approaches. A comparative analysis with existing baseline models highlights the superior performance of the proposed algorithms in the early detection and prediction of frailty. These findings contribute significantly to advancing the field and lay a foundation for future research aimed at implementing advanced clinical decision support systems ingeriatric care settings.