Field-Programmable Gate Arrays (FPGAs) have emerged as a key technology for dependable and high-performance machine learning (ML) systems, offering reconfigurability, energy efficiency, and parallel processing capabilities. This chapter explores the role of FPGAs in ML applications, comparing their architecture and performance with traditional computing platforms such as GPUs, CPUs, and ASICs. It examines design considerations for ML accelerators, showcasing real-world applications where FPGAs enable enhanced computational efficiency. Dependability aspects, including reliability, fault tolerance, redundancy, security, and power management, are examined to underscore their importance in ensuring robust ML operations. Additionally, the chapter explores future trends and challenges, highlighting the advancements in FPGA technology and their adaptability to emerging machine learning workloads. A proposed high-performance computing (HPC) technique (dynamic resource allocation with real-time FPGA reconfiguration) is introduced to optimize scalability for ML applications. Research requirements, experimental setups, and expected outcomes are outlined, providing insights into the future trajectory of FPGA-based ML systems. This chapter aims to guide researchers and industry professionals in leveraging FPGA technology for next-generation ML solutions by addressing both performance and dependability.