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Detailed analysis of electronic waste (e-waste) generation and composition is of utmost importance for the proper management of growing e-waste stream worldwide, containing both hazardous and valuable materials. Considering the absence of such comprehensive and up-to-date studies in Canada, this work presents the first estimate of put-on-market electrical and electronic equipment (EEE), the in-use stocks of EEE and e-waste generation in Canada from 1971 to 2030 for 51 product categories comprising 198 product types. Using a dynamic material flow analysis (MFA), the put-on-market EEE is estimated based on trade data retrieved from national and international import and export statistics, and the in-use stocks of EEE and the resulting e-waste are calculated using the Weibull distribution function. The results show that the total mass of EEE within the 60-year period is estimated to be 42.3 million tonnes, with an annual average growth rate of approximately 0.5%. By 2030, the total accumulated in-use stock of EEE is estimated to exceed 13 million tonnes. The estimated e-waste over the 60-year timespan is 29.1 million tonnes. The total annual e-waste generation in Canada is calculated to be 252 kilo tonnes (kt) and 954 kt in the years 2000 and 2020 respectively, which is estimated to reach 1.2 million tonnes by 2030. The e-waste generation per capita increased from 8.3 kg in 2000 to 25.3 kg in 2020 and is estimated to reach 31.5 kg by 2030. This quantification provides valuable insights to policymakers for setting up targets for waste reduction and identifying the resource circularity potential for efficient management of e-waste. [Display omitted] • Estimation of e-waste in Canada for 51 product categories over the 60-year timespan using Material Flow Analysis (MFA). • The annual e-waste generation in Canada is estimated to be 1.2 million tons by 2030. • The total accumulated in-use stock is estimated to exceed 13 million tonnes by 2030. • Large household appliances comprise 36% of e-waste by mass and medical devices comprise less than 0.1%. • Empirical data on consumer behavior is needed for more robust future estimates. [ABSTRACT FROM AUTHOR]