<p>The study of sequence convergence and summability has made significant strides, yet exploring alternative frameworks remains a promising avenue of research. This paper introduces a novel approach to the convergence and boundedness of triple sequences, utilizing Bessel functions in combination with modulus functions. This framework provides a refined analytical perspective on sequence behavior in higher dimensions. We construct new sequence spaces based on strong Bessel summability and investigate their structural properties, such as inclusion relations, completeness, and other key characteristics. Additionally, we incorporate Artificial Neural Networks (ANNs) for the classification of sequences generated by linear, quadratic, and exponential functions. Through the integration of ANN models, we demonstrate their ability to classify these sequences with high accuracy, showcasing the potential of machine learning techniques in advancing sequence space theory. Several illustrative examples are provided to highlight the effectiveness of both the theoretical and computational frameworks. The results of this study contribute to the ongoing development of sequence space theory, with implications for future research in multidimensional summability, convergence analysis, and machine learning applications in sequence classification.</p>