This study analysed the influence of socio-economic factors such as income, number of people in a household, education level, age, and gender of the household head on the quantity and composition of solid waste generated in Kampala City. Three areas: Bwaise I (low-income), Bukoto I (medium-income), and Ggaba (high-income) were considered. A quantitative, source-sampling approach was adopted, involving household surveys and measurement of domestic waste generated over 7 days. Of the 139 households enrolled, 103 (74%) provided complete data and were analysed (39 low-, 32 middle-, and 32 high-income households). High-income areas generated the largest amount of waste (0.98 ± 0.42 kg/ca. day; 95% CI: 0.83–1.13), followed by middle-income (0.53 ± 0.22 kg/ca. day; 95% CI: 0.45–0.61) and low-income areas (0.43 ± 0.35 kg/ca. day; 95% CI: 0.32–0.54). Organic waste was the dominant fraction across all income groups, accounting for an average of 78.6% ± 0.03% (95% CI: 75.33–81.93), with 78.51% ± 0.27% (95% CI: 70.46–86.97), 81.92% ± 0.142% (95% CI: 77.07–85.74) and 75.37% ± 0.125% (95% CI: 71.29–78.96) in low-, middle-, and high-income areas respectively. The average density of the waste was 368 ± 138 kg/m3 (95% CI: 323.2–413.1), 571 ± 190 kg/m3 (95% CI: 502.9–639.6) and 325 kg/m3 ± 44 kg/m3 (95% CI: 309.1–340.9) in low-, middle- and high-income areas respectively. This rules out compactor trucks, since the density of the waste is nearly similar to that achievable by truck compaction. Per capita solid waste generation differed significantly by household income (Kruskal–Wallis H (2) = 34.17, p < 0.001, ε2 = 0.32) and by the education level of the household head (H (3) = 24.75, p < 0.001, ε2 = 0.22). Household size showed a small but significant negative independent effect (β = −0.032 kg/ca. day per additional member, 95% CI: −0.059 to −0.005, p = 0.021), consistent with economies of scale in shared consumption (adjusted R2 = 0.34). The identified relationships between household socio-economics and solid waste characteristics are relevant for formulating solid waste management strategies contextually suitable for Kampala or similar urban and peri-urban settings. Besides, more up-to-date data on solid waste generation and composition are necessary to support improved planning for solid waste collection, treatment and resource recovery.