In our research, we investigate the combination of game theory principles with machine learning algorithms, in particular, Artificial Neural Network, aiming to improve the effectiveness of smart home security systems. Based on the thorough experimentation and analysis, we found that game theory integration with the ANN-centric framework indeed has the potential to positively transform the intruder detection. Across the board, the integrated system showed significant improvement over the standalone approach. Response time, for instance, decreased to 30 s from 45 s, and time required to initiate actions was reduced to 1 min from 2 min. In addition, payoff on ANN increased to 0.85 from 0.65, indicating improved efficiency in predicting intrusion response. Detection rate was also superior in the integrated system, with the amount of 97% compared to 94% in standalone, and false alarm rate lower at 5%, as opposed to 8%. Throughout the research, it was found that game theory integration with ANN holds significant promise in terms of transforming traditional intrusion detection systems in smart homes. By leveraging the predictive capabilities of ANN and the strategic insights afforded by game theory, the integrated solution both enhances the operation of the infrastructure and results in a robust security environment capable of proactive intruder detection and targeting responses. Not only does it allow the improvement of response times and accuracy of detection, but it will also provide a means to fend off the emerging threats as the landscape of smart home security continues to transform.