This study was an examination of digital and intelligent approach for electric vehicle integration and vehicle-to-grid interaction: prediction, evaluation, flexibility and resiliency. The diffusion of innovation theory was anchored in this study. This study adopted a qualitative research approach to explore the integration of electric vehicles (EVs) and Vehicle-to-Grid (V2G) interaction, focusing on prediction, evaluation, flexibility, and resiliency. The population of the study consisted of stakeholders within the energy and transportation sectors in Nigeria, including representatives from government regulatory agencies, utility companies, EV manufacturers, and charging infrastructure providers. Given the focus on obtaining rich, descriptive data, a purposive sampling technique was used to select 10 participants who have direct experience or expertise in EV adoption, grid management, and energy policy. Data were collected through semi-structured interviews and focus group discussions, allowing participants to share their perspectives on EV integration challenges, policy frameworks, and technological advancements. The semi-structured interview guide covered key themes such as technological readiness, regulatory barriers, economic implications, and social acceptance of EV-grid interaction. Interviews and focus groups were conducted in person and virtually, depending on participant availability and location. Data analysis was done using thematic analysis, where responses were be coded and categorised into themes that reflect recurring patterns and insights. The findings revealed that that the integration of digital and intelligent technologies, such as AI and machine learning, significantly enhances the prediction and evaluation of electric vehicle (EV) integration into the power grid by enabling real-time monitoring, data-driven decision-making, and efficient management of energy demand, which optimises grid capacity and infrastructure planning while mitigating potential grid stress during peak charging periods. The study concluded that the integration of digital and intelligent technologies, such as AI and machine learning, plays a pivotal role in optimising the prediction and evaluation of EV integration into the power grid, offering grid operators the tools to manage energy demand effectively, prevent grid overloads, and enhance long-term sustainability. The study recommended that utilities should invest in advanced digital technologies such as AI and machine learning to enhance real-time data analysis and optimise EV integration, ensuring efficient grid management and long-term sustainability