As applications such as AI and VLSI demand faster and more energy-efficient computing, there has been an growing extension of research into estimate computing technique. This paper presents the High-Speed Digital Approximation Multiplier Design and Performance Study for AI-VLSI Systems. By intentionally reducing the precision of certain arithmetic operations, the proposed multiplier provides significant performance and energy efficiency improvements with an accuracy level appropriate for generic AI benchmarks. By exploring new error-tolerant algorithms, the architecture balances trade-offs of speed, power consumption, and computing precision. The approximate multiplier suggested here reduces power consumption by about 40% and operating speed by 30% compared to traditional multipliers according to simulation, without significant impact on the performance of AI models. The research also compares different approximation levels to find the best setup that best fits the certain AI-VLSI applications. This research presents a 64-bit approximate multiplier based on the dadda multipliers and compressors that achieves high accuracy and speed. Approximate multiplier is one of the fastest multipliers for AI based FPGA-VLSI applications. In the proposed research, a near multiplier of 64 × 64 bits is shows. An FPGA IC from the Virtex 7 family is used to simulate the results.