Artificial intelligence (AI) is revolutionizing multiple-input-multiple-output (MIMO) technology, making it a promising contender for the coming sixth-generation (6G) and beyond-fifth-generation (B5G) networks. However, the detection process in MIMO systems is highly complex and computationally demanding. To address this challenge, this paper presents an optimized AI-based signal detection method known as AIDETECT-2 which is based on feed forward neural network (FFNN) for MIMO systems. The proposed AIDETECT-2 network model demonstrates superior efficiency in signal detection in comparison with conventional and AI-based MIMO detection methods, particularly in terms of symbol error rate (SER) at various signal-to-noise ratios (SNR). This paper thoroughly explores various signal detection aspects using FFNN, including the design of system architecture, preparation of data, training processes of the network model, and performance evaluation. Simulation results show that the proposed model demonstrates a significant performance improvement ranging between 13.75% to 99.995% better SER compared to the best conventional method and also achieved between 56.52% to 97.69 better SER compared to benchmark AI-based MIMO detectors at 20 dB SNR for given MIMO scenarios respectively. It also presented the computational complexity analysis of different conventional and AI-based MIMO detectors. We believe that this optimized AI-based network model can serve as a comprehensive guide for deploying deep-learning (DL) neural networks for signal detection in the forthcoming 6G wireless networks.