Artificial intelligence (AI) has become an important technology for improving the performance of modern wireless communication systems. Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) is widely used in advanced wireless networks because it improves spectral efficiency, data transmission reliability, and communication capacity. However, these systems face several challenges, including channel fading, interference, limited bandwidth, and energy consumption. This article proposes an AI-based approach to optimize the performance of MIMO-OFDM wireless communication systems. Machine learning techniques can be used to select appropriate transmission parameters, improve resource allocation, and enhance communication quality under different channel conditions. The proposed approach can be evaluated through MATLAB simulations using performance indicators such as Bit Error Rate (BER), throughput, and signal-to-noise ratio (SNR). The results can be compared with conventional optimization methods to determine the effectiveness of artificial intelligence in wireless communication. This approach provides a promising direction for developing intelligent and efficient communication systems for future wireless networks. Keywords: Artificial Intelligence, MIMO-OFDM, Wireless Communication, Machine Learning, MATLAB, BER, Throughput. 1. Introduction Wireless communication systems have experienced significant development due to the increasing demand for high data rates, reliable connectivity, and efficient spectrum utilization. Modern communication technologies, including fifth-generation (5G) networks and emerging sixth-generation (6G) systems, require advanced techniques to improve transmission quality and reduce communication delays. MIMO-OFDM technology combines multiple antennas with orthogonal frequency division multiplexing to increase data transmission capacity and improve resistance to multipath fading. Despite these advantages, achieving optimal system performance remains challenging because wireless channels change over time and are affected by noise, interference, and environmental conditions. Artificial intelligence provides new opportunities to address these challenges. Machine learning algorithms can analyze communication data, recognize channel patterns, and support decisions related to transmission parameters and resource allocation. Integrating AI into MIMO-OFDM systems can therefore improve communication performance and reduce the need for repeated manual parameter adjustment. 2. Proposed Methodology The proposed system consists of a transmitter, a MIMO-OFDM communication channel, a receiver, and an artificial intelligence optimization module. The transmitter generates digital information and modulates it using an appropriate modulation scheme. OFDM processing distributes the information across multiple orthogonal subcarriers, while multiple antennas support spatial transmission. The transmitted signal passes through a wireless channel affected by noise and fading. At the receiver, the signal is processed to recover the original information. The AI optimization module analyzes system parameters and selects suitable transmission settings according to the required performance objectives. The system can be implemented in MATLAB by generating random binary data, applying digital modulation, performing OFDM modulation, simulating the wireless channel, and recovering the transmitted data. An optimization algorithm can then be introduced to improve selected system parameters. The performance of the proposed method should be evaluated under different SNR values and channel conditions. Conventional and AI-based approaches can be compared to determine their effects on BER, throughput, and energy efficiency. 3. Performance Evaluation The proposed system can be evaluated using several important performance indicators. Bit Error Rate measures the proportion of incorrectly received bits and provides an indication of communication reliability. Throughput represents the amount of successfully transmitted information per unit of time. Signal-to-noise ratio describes the strength of the received signal relative to background noise. MATLAB simulations can be used to generate BER-versus-SNR curves and compare the system under different configurations. The effectiveness of the AI-based method must be established through actual simulation results rather than assumed improvements. 4. Conclusion Artificial intelligence offers promising opportunities to improve the performance of MIMO-OFDM wireless communication systems. By supporting intelligent parameter selection and resource optimization, AI-based techniques may improve transmission reliability and communication efficiency. MATLAB simulation provides a practical environment for investigating these techniques and comparing their performance with conventional methods. Future research can investigate deep learning and reinforcement learning algorithms for adaptive resource allocation and intelligent wireless communication.