Metallic medical implants play a critical role in re-establishing physiological purpose and structural balance across various clinical appeals, involving orthopedics and dentistry. However, the long-term success of these implants is remarkably compromised by corrosion processes occurring in physiological environments. The purpose of this review is to thoroughly examine the mechanisms underlying corrosion induced deterioration in widely used implant material, including titanium alloy, cobalt-chromium alloys, and stainless steel, and to determine the clinical implications of these mechanisms. A thorough review of the literature was done with an emphasis on surface characterization, biological reaction, including cytotoxicity linked to metal ion release, and electrochemical corrosion behaviors. The main conclusions show that stainless steel is the most prone to corrosion, especially in environment with a lot of chloride. This is mainly because titanium alloys, which have a stable and protective TiO₂ passive layer, are more resistant to corrosion than stainless steel. Despite titanium’s superior corrosion resistance, all implant materials exhibit vulnerability to mechanical stress, fretting, and harsh biological condition. The significant association between the release of metal ions such as nickel and cobalt and increased inflammatory responses and decreased cell viability raise serious concern about patient safety. Current mitigation technique such as surface passivation, polymer-based barriers hydroxyapatite coating, and advanced coating like titanium nitride could improve the biocompatibility and durability of implant. To guarantee sustained performance, however, additional optimization and thorough long-term on vivo assessments are required. The review highlight how essential better metal ion discharged regulations, standardized corrosion testing process, and the creation of innovative hybrid materials with elevated corrosion resistance are needed. In order to enhance implant longevity, safety, and regulatory compliance, future research that merge clinical data with predictive modeling will be necessary.