Abstract

: Neuro‑symbolic computing models represent a hybrid paradigm that unifies symbolic reasoning with neural network learning to address limitations of purely connectionist or purely symbolic approaches in artificial intelligence. As intelligent systems proliferate in domains requiring transparency and accountability — such as healthcare, law, and autonomous systems — the need for both explainability and interpretability becomes paramount. Traditional deep learning models often achieve high performance but lack structured reasoning and human‑readable explanations. Symbolic reasoning systems, conversely, provide interpretability but struggle with learning from raw data and generalizing in complex environments. Neuro‑symbolic computing seeks to bridge these gaps by embedding structured symbolic knowledge into learning architectures, enabling systems to combine the robustness of statistical learning with the clarity of symbolic logic. This research explores foundational theories, architectural frameworks, and practical implementations of neuro‑symbolic models, examines how they contribute to explainable and interpretable intelligent systems, and evaluates their strengths and limitations. Through systematic synthesis of existing research and comparative analysis of representative models, the study highlights how neuro‑symbolic methodologies can enhance reasoning, support compositional generalization, and produce explanations that align with human cognitive processes. Challenges remain in scalability, knowledge representation integration, and evaluation metrics for interpretability. Future research directions emphasize standardized benchmarks, hybrid learning strategies, and domain‑specific adaptations.