With more than 80% of Malaysia’s fields relying on gas lift, addressing late-life challenges becomes imperative, necessitating enhancements in secondary and tertiary production to sustain oil production. Issues such as gas lift gas shortages, aging infrastructure, and rising water cut impose constraints on efficient oil recovery, driving the quest for alternative lift technologies in offshore Malaysian brown fields. Nonetheless, replacing gas lift systems is challenging. This study advocates an automated pre-screening approach to swiftly identify candidates, minimizing the time and manpower required to sift through vast number of wells, thus boost the production optimization endeavors for offshore brown fields in Malaysia. The proposed automation system evaluated data from multiple fields (Field B, D and S) to pre-screen strings and identify opportunities before advancing to detailed screening using well modeling. From the array of attributes analyzed, crucial parameters for candidate pre-screening identified. Automating this pre-screening data narrows down potential candidates for in-depth analysis, facilitating ESP design, economic assessment, and other production enhancement activities. By digitizing this process, automation simplifies the identification of enhancement candidates, saving time and expanding the pool of candidates available for production enhancement initiatives. Furthermore, this automation method extends beyond ESP candidates to encompass acid stimulation, gas lift optimization, and water shut-off candidates. By efficiently processing and analyzing data, this automated pre-screening approach offers significant time savings, identifies a pool of candidates, and uncovers opportunities for production enhancement activities, thereby revolutionizing the well modeling process and enhancing decision-making throughout candidate maturation.