This paper explores the detection prospects of gravitational wave (GW) memory using the Laser Interferometer Space Antenna (LISA). GW memory, interpreted as a permanent deformation of spacetime after a GW perturbation, offers a novel avenue to test General Relativity, access the non-linear nature of gravity, and provide complementary information about GW sources. The authors simulate the time-domain response of LISA to GW memory, using the most up-to-date LISA consortium simulations and numerical relativity-based time-series computation. They compare the LISA observation window with the population of massive black hole binary mergers forecasted by state-of-the-art models and evaluate the odds and expected accuracies for detecting GW memory. The study concludes that GW memory will be a key feature of several events detected by LISA, enhancing the scientific potential of the mission. The paper also discusses the dependence of the memory SNR on sky position, binary parameters, and astrophysical population models, highlighting the importance of considering these factors for future data analysis strategies.This paper explores the detection prospects of gravitational wave (GW) memory using the Laser Interferometer Space Antenna (LISA). GW memory, interpreted as a permanent deformation of spacetime after a GW perturbation, offers a novel avenue to test General Relativity, access the non-linear nature of gravity, and provide complementary information about GW sources. The authors simulate the time-domain response of LISA to GW memory, using the most up-to-date LISA consortium simulations and numerical relativity-based time-series computation. They compare the LISA observation window with the population of massive black hole binary mergers forecasted by state-of-the-art models and evaluate the odds and expected accuracies for detecting GW memory. The study concludes that GW memory will be a key feature of several events detected by LISA, enhancing the scientific potential of the mission. The paper also discusses the dependence of the memory SNR on sky position, binary parameters, and astrophysical population models, highlighting the importance of considering these factors for future data analysis strategies.