The onslaught of artificial intelligence (AI) in the global scientific and industrial landscape has brought with it far-reaching implications into how the software development process can be transformed. This article presents a systematic literature review focused on the integration of AI into the software development life cycle (SDLC) with a specific emphasis on sustainability. The research explores the application of AI in all facets of the SDLC. To this end, we structure 34 primary studies into the different stages of the SDLC, including requirements elicitation, analysis/design, development, testing, and deployment, while considering multiple dimensions of sustainability. Our findings present a synthesis of commonly used AI approaches under various aspects. These encompass guidelines for (i) automating requirements formulation, (ii) designing sustainable software, (iii) enhancing energy efficiency and code reuse, and (iv) effectively testing software. Environmental sustainability was found to be the most common dimension in the literature, primarily addressing energy efficiency and electronic waste. Additionally, we identify gaps in the literature, particularly the absence of addressing AI in SDLC from the angle of social sustainability and the lack of integration into developer toolkits.